System and method for using ai, machine learning and telemedicine to perform pulmonary rehabilitation via an electromechanical machine

ABSTRACT

A computer-implemented system includes one or more processing devices configured to receive attribute data associated with a user, generate, based on a pulmonary condition of the user, a selected set of the attribute data, determine, based on the selected set of the attribute data, a first probability of improving a pulmonary condition of the user subsequent to at least one of a pulmonary procedure being performed on the user, a pulmonary treatment being performed on the user, and a pulmonary diagnosis, and generate, based on the first probability, a treatment plan that includes one or more exercises directed to modifying the first probability. A treatment apparatus is configured to enable implementation of the treatment plan.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of and claims priority to andthe benefit of U.S. patent application Ser. No. 17/736,891, filed May 4,2022 (Attorney Docket No. 91346-15100), titled “Systems and Methods forUsing Artificial Intelligence to Implement a Cardio Protocol via aRelay-Based System,” which is a continuation-in-part of and claimspriority to and the benefit of U.S. patent application Ser. No.17/379,542, filed Jul. 19, 2021 (Attorney Docket No. 91346-5702), titled“System and Method for Using Artificial Intelligence inTelemedicine-Enabled Hardware to Optimize Rehabilitative RoutinesCapable of Enabling Remote Rehabilitative Compliance” (now U.S. Pat. No.11,328,807, issued May 10, 2022), which is a continuation of and claimspriority to and the benefit of U.S. patent application Ser. No.17/146,705, filed Jan. 12, 2021 (Attorney Docket No. 91346-5701), titled“System and Method for Using Artificial Intelligence inTelemedicine-Enabled Hardware to Optimize Rehabilitative RoutinesCapable of Enabling Remote Rehabilitative Compliance,” which is acontinuation-in-part of and claims priority to and the benefit of U.S.patent application Ser. No. 17/021,895, filed Sep. 15, 2020 (AttorneyDocket No. 91346-1410), titled “Telemedicine for Orthopedic Treatment”(now U.S. Pat. No. 11,071,597, issued Jul. 27, 2021), which claimspriority to and the benefit of U.S. Provisional Patent Application Ser.No. 62/910,232, filed Oct. 3, 2019 (Attorney Docket No. 91346-1400),titled “Telemedicine for Orthopedic Treatment,” the entire disclosuresof which are hereby incorporated by reference for all purposes. Theapplication U.S. patent application Ser. No. 17/146,705 also claimspriority to and the benefit of U.S. Provisional Patent Application Ser.No. 63/113,484, filed Nov. 13, 2020 (Attorney Docket No. 91346-5700),titled “System and Method for Use of Artificial Intelligence inTelemedicine-Enabled Hardware to Optimize Rehabilitative Routines forEnabling Remote Rehabilitative Compliance,” the entire disclosures ofwhich are hereby incorporated by reference for all purposes.

This application also claims priority to and the benefit of U.S.Provisional Patent Application Ser. No. 63/407,049 filed Sep. 15, 2022,titled “Systems and Methods for Using Artificial Intelligence and anElectromechanical Machine to Aid Rehabilitation in Various PatientMarkets,” the entire disclosure of which is hereby incorporated byreference for all purposes.

BACKGROUND

Remote medical assistance, also referred to, inter alia, as remotemedicine, telemedicine, telemed, telmed, tel-med, or telehealth, is anat least two-way communication between a healthcare professional orproviders, such as a physician or a physical therapist, and a patientusing audio and/or audiovisual and/or other sensorial or perceptive(e.g., tactile, gustatory, haptic, pressure-sensing-based orelectromagnetic (e.g., neurostimulation) communications (e.g., via acomputer, a smartphone, or a tablet). Telemedicine may aid a patient inperforming various aspects of a rehabilitation regimen for a body part.The patient may use a patient interface in communication with anassistant interface for receiving the remote medical assistance viaaudio, visual, audiovisual, or other communications described elsewhereherein. Any reference herein to any particular sensorial modality shallbe understood to include and to disclose by implication a different oneor more sensory modalities.

Telemedicine is an option for healthcare professionals to communicatewith patients and provide patient care when the patients do not want toor cannot easily go to the healthcare professionals' offices.Telemedicine, however, has substantive limitations as the healthcareprofessionals cannot conduct physical examinations of the patients.Rather, the healthcare professionals must rely on verbal communicationand/or limited remote observation of the patients.

Cardiovascular health refers to the health of the heart and bloodvessels of an individual. Cardiovascular diseases or cardiovascularhealth issues include a group of diseases of the heart and bloodvessels, including coronary heart disease, stroke, heart failure, heartarrhythmias, and heart valve problems. It is generally known thatexercise and a healthy diet can improve cardiovascular health and reducethe chance or impact of cardiovascular disease.

Various other markets are related to health conditions associated withother portions and/or systems of a human body. For example, otherprevalent health conditions pertain to pulmonary health, bariatrichealth, oncologic health, prostate health, and the like. There is alarge portion of the population who are affected by one or more of thesehealth conditions. Treatment and/or rehabilitation for the healthconditions, as currently provided, is not adequate to satisfy themassive demand prevalent in the population worldwide.

SUMMARY

A computer-implemented system includes one or more processing devicesconfigured to receive attribute data associated with a user, generate,based on a pulmonary condition of the user, a selected set of theattribute data, determine, based on the selected set of the attributedata, a first probability of improving a pulmonary condition of the usersubsequent to at least one of a pulmonary procedure being performed onthe user, a pulmonary treatment being performed on the user, and apulmonary diagnosis, and generate, based on the first probability, atreatment plan that includes one or more exercises directed to modifyingthe first probability. A treatment apparatus is configured to enableimplementation of the treatment plan.

Another aspect of the disclosed embodiments includes a system thatincludes a processing device and a memory communicatively coupled to theprocessing device and capable of storing instructions. The processingdevice executes the instructions to perform any of the methods,operations, or steps described herein.

Another aspect of the disclosed embodiments includes a tangible,non-transitory computer-readable medium storing instructions that, whenexecuted, cause a processing device to perform any of the methods,operations, or steps disclosed herein.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description, the claims, and the drawings.The detailed description and specific examples are intended for purposesof illustration only and are not intended to limit the scope of thedisclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure is best understood from the following detaileddescription when read in conjunction with the accompanying drawings. Itis emphasized that, according to common practice, the various featuresof the drawings are not to-scale. On the contrary, the dimensions of thevarious features are arbitrarily expanded or reduced for clarity.

For a detailed description of example embodiments, reference will now bemade to the accompanying drawings in which:

FIG. 1 generally illustrates a block diagram of an embodiment of acomputer implemented system for managing a treatment plan according tothe principles of the present disclosure;

FIG. 2 generally illustrates a perspective view of an embodiment of atreatment apparatus according to the principles of the presentdisclosure;

FIG. 3 generally illustrates a perspective view of a pedal of thetreatment apparatus of FIG. 2 according to the principles of the presentdisclosure;

FIG. 4 generally illustrates a perspective view of a person using thetreatment apparatus of FIG. 2 according to the principles of the presentdisclosure;

FIG. 5 generally illustrates an example embodiment of an overviewdisplay of an assistant interface according to the principles of thepresent disclosure;

FIG. 6 generally illustrates an example block diagram of training amachine learning model to output, based on data pertaining to thepatient, a treatment plan for the patient according to the principles ofthe present disclosure;

FIG. 7 generally illustrates an embodiment of an overview display of theassistant interface presenting recommended treatment plans and excludedtreatment plans in real-time during a telemedicine session according tothe principles of the present disclosure;

FIG. 8 generally illustrates an example embodiment of a method foroptimizing a treatment plan for a user to increase a probability of theuser complying with the treatment plan according to the principles ofthe present disclosure;

FIG. 9 generally illustrates an example embodiment of a method forgenerating a treatment plan based on a desired benefit, a desired painlevel, an indication of probability of complying with a particularexercise regimen, or some combination thereof according to theprinciples of the present disclosure;

FIG. 10 generally illustrates an example embodiment of a method forcontrolling, based on a treatment plan, a treatment apparatus while auser uses the treatment apparatus according to the principles of thepresent disclosure;

FIG. 11 generally illustrates an example computer system according tothe principles of the present disclosure;

FIG. 12 generally illustrates a perspective view of a person using thetreatment apparatus of FIG. 2 , the patient interface 50, and acomputing device according to the principles of the present disclosure;

FIG. 13 generally illustrates a display of the computing devicepresenting a treatment plan designed to improve the user'scardiovascular health according to the principles of the presentdisclosure;

FIG. 14 generally illustrates an example embodiment of a method forgenerating treatment plans including sessions designed to enable a userto achieve a desired exertion level based on a standardized measure ofperceived exertion according to the principles of the presentdisclosure;

FIG. 15 generally illustrates an example embodiment of a method forreceiving input from a user and transmitting the feedback to be used togenerate a new treatment plan according to the principles of the presentdisclosure;

FIG. 16 generally illustrates an example embodiment of a method forimplementing a cardiac rehabilitation protocol by using artificialintelligence and a standardized measurement according to the principlesof the present disclosure;

FIG. 17 generally illustrates an example embodiment of a method forenabling communication detection between devices and performance of apreventative action according to the principles of the presentdisclosure;

FIG. 18 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning to detect abnormalheart rhythms of a user performing a treatment plan via anelectromechanical machine according to the principles of the presentdisclosure;

FIG. 19 generally illustrates an example embodiment of a method forresidentially-based cardiac rehabilitation by using an electromechanicalmachine and educational content to mitigate risk factors and optimizeuser behavior according to the principles of the present disclosure;

FIG. 20 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning and telemedicine toperform bariatric rehabilitation via an electromechanical machineaccording to the principles of the present disclosure;

FIG. 21A generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning and telemedicine toperform pulmonary rehabilitation via an electromechanical machineaccording to the principles of the present disclosure;

FIG. 21B generally illustrates a block diagram of an embodiment of acomputer-implemented system for performing pulmonary rehabilitation viaan electromechanical machine according to the principles of the presentdisclosure;

FIG. 21C generally illustrates an example method for performingpulmonary rehabilitation via an electromechanical machine according tothe principles of the present disclosure;

FIG. 22 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning and telemedicine toperform cardio-oncologic rehabilitation via an electromechanical machineaccording to the principles of the present disclosure;

FIG. 23 generally illustrates an example embodiment of a method foridentifying subgroups, determining cardiac rehabilitation eligibility,and prescribing a treatment plan for the eligible subgroups according tothe principles of the present disclosure;

FIG. 24 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning to provide anenhanced user interface presenting data pertaining to cardiac health,bariatric health, pulmonary health, and/or cardio-oncologic health forthe purpose of performing preventative actions according to theprinciples of the present disclosure;

FIG. 25 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning and telemedicine forlong-term care via an electromechanical machine according to theprinciples of the present disclosure;

FIG. 26 generally illustrates an example embodiment of a method forassigning users to be monitored by observers where the assignment andmonitoring are based on promulgated regulations according to theprinciples of the present disclosure;

FIG. 27 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning and telemedicine forcardiac and pulmonary treatment via an electromechanical machine ofsexual performance according to the principles of the presentdisclosure;

FIG. 28 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning and telemedicine forprostate-related oncologic or other surgical treatment to determine acardiac treatment plan that uses via an electromechanical machine, andwhere erectile dysfunction is secondary to the prostate treatment and/orcondition according to the principles of the present disclosure;

FIG. 29 generally illustrates an example embodiment of a method fordetermining, based on advanced metrics of actual performance on anelectromechanical machine, medical procedure eligibility in order toascertain survivability rates and measures of quality of life criteriaaccording to the principles of the present disclosure;

FIG. 30 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning and telemedicine tointegrate rehabilitation for a plurality of comorbid conditionsaccording to the principles of the present disclosure;

FIG. 31 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning and generic riskfactors to improve cardiovascular health such that the need for cardiacintervention is mitigated according to the principles of the presentdisclosure;

FIG. 32 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning to generate treatmentplans to stimulate preferred angiogenesis according to the principles ofthe present disclosure;

FIG. 33 generally illustrates an example embodiment of a method forusing artificial intelligence and machine learning to generate treatmentplans including tailored dietary plans for users according to theprinciples of the present disclosure;

FIG. 34 generally illustrates an example embodiment of a method forpresenting an enhanced healthcare professional user interface displayingmeasurement information for a plurality of users according to theprinciples of the present disclosure;

FIG. 35 generally illustrates an embodiment of an enhanced healthcareprofessional display of the assistant interface presenting measurementinformation for a plurality of patients concurrently engaged intelemedicine sessions with the healthcare professional according to theprinciples of the present disclosure;

FIG. 36 generally illustrates an example embodiment of a method forpresenting an enhanced patient user interface displaying real-timemeasurement information during a telemedicine session according to theprinciples of the present disclosure; and

FIG. 37 generally illustrates an embodiment of an enhanced patientdisplay of the patient interface presenting real-time measurementinformation during a telemedicine session according to the principles ofthe present disclosure.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components.Different companies may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . .” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

The terminology used herein is for the purpose of describing particularexample embodiments only, and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The terms “a,” “an,” “the,” and “said” as used herein inconnection with any type of processing component configured to performvarious functions may refer to one processing component configured toperform each and every function, or a plurality of processing componentscollectively configured to perform each of the various functions. By wayof example, “A processor” configured to perform actions A, B, and C mayrefer to one processor configured to perform actions A, B, and C. Inaddition, “A processor” configured to perform actions A, B, and C mayalso refer to a first processor configured to perform actions A and B,and a second processor configured to perform action C. Further, “Aprocessor” configured to perform actions A, B, and C may also refer to afirst processor configured to perform action A, a second processorconfigured to perform action B, and a third processor configured toperform action C. The method steps, processes, and operations describedherein are not to be construed as necessarily requiring theirperformance in the particular order discussed or illustrated, unlessspecifically identified as an order of performance. It is also to beunderstood that additional or alternative steps may be employed. As usedwith respect to occurrence of an action relative to another action, theterm “if” may be interpreted as “in response to.”

The terms first, second, third, etc. may be used herein to describevarious elements, components, regions, layers and/or sections; however,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer, or section from another region,layer, or section. Terms such as “first,” “second,” and other numericalterms, when used herein, do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer, or section discussed below could be termed a second element,component, region, layer, or section without departing from theteachings of the example embodiments. The phrase “at least one of,” whenused with a list of items, means that different combinations of one ormore of the listed items may be used, and only one item in the list maybe needed. For example, “at least one of: A, B, and C” includes any ofthe following combinations: A, B, C, A and B, A and C, B and C, and Aand B and C. In another example, the phrase “one or more” when used witha list of items means there may be one item or any suitable number ofitems exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” “top,” “bottom,” “inside,” “outside,”“contained within,” “superimposing upon,” and the like, may be usedherein. These spatially relative terms can be used for ease ofdescription to describe one element's or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms may also be intended to encompass differentorientations of the device in use, or operation, in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” the other elementsor features. Thus, the example term “below” can encompass both anorientation of above and below. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptions used herein interpreted accordingly.

A “treatment plan” may include one or more treatment protocols orexercise regimens, and each treatment protocol or exercise regimen mayinclude one or more treatment sessions or one or more exercise sessions.Each treatment session or exercise session may comprise one or moresession periods or exercise periods, where each session period orexercise period may include at least one exercise for treating the bodypart of the patient. In some embodiments, exercises that improve thecardiovascular health of the user are included in each session. For eachsession, exercises may be selected to enable the user to perform atdifferent exertion levels. The exertion level for each session may bebased at least on a cardiovascular health issue of the user and/or astandardized measure comprising a degree, characterization or otherquantitative or qualitative description of exertion. The cardiovascularhealth issues may include, without limitation, heart surgery performedon the user, a heart transplant performed on the user, a heartarrhythmia of the user, an atrial fibrillation of the user, tachycardia,bradycardia, supraventricular tachycardia, congestive heart failure,heart valve disease, arteriosclerosis, atherosclerosis, pericardialdisease, pericarditis, myocardial disease, myocarditis, cardiomyopathy,congenital heart disease, or some combination thereof. Thecardiovascular health issues may also include, without limitation,diagnoses, diagnostic codes, symptoms, life consequences, comorbidities,risk factors to health, life, etc. The exertion levels may progressivelyincrease between each session. For example, an exertion level may be lowfor a first session, medium for a second session, and high for a thirdsession. The exertion levels may change dynamically during performanceof a treatment plan based on at least cardiovascular data received fromone or more sensors, the cardiovascular health issue, and/or thestandardized measure comprising a degree, characterization or otherquantitative or qualitative description of exertion. Any suitableexercise (e.g., muscular, weight lifting, cardiovascular, therapeutic,neuromuscular, neurocognitive, meditating, yoga, stretching, etc.) maybe included in a session period or an exercise period. For example, atreatment plan for post-operative rehabilitation after a knee surgerymay include an initial treatment protocol or exercise regimen with twicedaily stretching sessions for the first 3 days after surgery and a moreintensive treatment protocol with active exercise sessions performed 4times per day starting 4 days after surgery. A treatment plan may alsoinclude information pertaining to a medical procedure to perform on thepatient, a treatment protocol for the patient using a treatmentapparatus, a diet regimen for the patient, a medication regimen for thepatient, a sleep regimen for the patient, additional regimens, or somecombination thereof.

The terms telemedicine, telehealth, telemed, teletherapeutic,telemedicine, remote medicine, etc. may be used interchangeably herein.

The term “optimal treatment plan” may refer to optimizing a treatmentplan based on a certain parameter or factors or combinations of morethan one parameter or factor, such as, but not limited to, a measure ofbenefit which one or more exercise regimens provide to users, one ormore probabilities of users complying with one or more exerciseregimens, an amount, quality or other measure of sleep associated withthe user, information pertaining to a diet of the user, informationpertaining to an eating schedule of the user, information pertaining toan age of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user, anindication of an energy level of the user, information pertaining to amicrobiome from one or more locations on or in the user (e.g., skin,scalp, digestive tract, vascular system, etc.), or some combinationthereof.

As used herein, the term healthcare professional may include a medicalprofessional (e.g., such as a doctor, a physician assistant, a nursepractitioner, a nurse, a therapist, and the like), an exerciseprofessional (e.g., such as a coach, a trainer, a nutritionist, and thelike), or another professional sharing at least one of medical andexercise attributes (e.g., such as an exercise physiologist, a physicaltherapist, a physical therapy technician, an occupational therapist, andthe like). As used herein, and without limiting the foregoing, a“healthcare professional” may be a human being, a robot, a virtualassistant, a virtual assistant in virtual and/or augmented reality, oran artificially intelligent entity, such entity including a softwareprogram, integrated software and hardware, or hardware alone.

Real-time may refer to less than or equal to 2 seconds. Near real-timemay refer to any interaction of a sufficiently short time to enable twoindividuals to engage in a dialogue via such user interface, and willpreferably but not determinatively be less than 10 seconds but greaterthan 2 seconds.

Any of the systems and methods described in this disclosure may be usedin connection with rehabilitation. Rehabilitation may be directed atcardiac rehabilitation, rehabilitation from stroke, multiple sclerosis,Parkinson's disease, myasthenia gravis, Alzheimer's disease, any otherneurodegenerative or neuromuscular disease, a brain injury, a spinalcord injury, a spinal cord disease, a joint injury, a joint disease,post-surgical recovery, or the like. Rehabilitation can further involvemuscular contraction in order to improve blood flow and lymphatic flow,engage the brain and nervous system to control and affect a traumatizedarea to increase the speed of healing, reverse or reduce pain (includingarthralgias and myalgias), reverse or reduce stiffness, recover range ofmotion, encourage cardiovascular engagement to stimulate the release ofpain-blocking hormones or to encourage highly oxygenated blood flow toaid in an overall feeling of well-being. Rehabilitation may be providedfor individuals of average weight in reasonably good physical conditionhaving no substantial deformities, as well as for individuals moretypically in need of rehabilitation, such as those who are elderly,obese, subject to disease processes, injured and/or who have a severelylimited range of motion. Unless expressly stated otherwise, is to beunderstood that rehabilitation includes prehabilitation (also referredto as “pre-habilitation” or “prehab”). Prehabilitation may be used as apreventative procedure or as a pre-surgical or pre-treatment procedure.Prehabilitation may include any action performed by or on a patient (ordirected to be performed by or on a patient, including, withoutlimitation, remotely or distally through telemedicine) to, withoutlimitation, prevent or reduce a likelihood of injury (e.g., prior to theoccurrence of the injury); improve recovery time subsequent to surgery;improve strength subsequent to surgery; or any of the foregoing withrespect to any non-surgical clinical treatment plan to be undertaken forthe purpose of ameliorating or mitigating injury, dysfunction, or othernegative consequence of surgical or non-surgical treatment on anyexternal or internal part of a patient's body. For example, a mastectomymay require prehabilitation to strengthen muscles or muscle groupsaffected directly or indirectly by the mastectomy. As a furthernon-limiting example, the removal of an intestinal tumor, the repair ofa hernia, open-heart surgery or other procedures performed on internalorgans or structures, whether to repair those organs or structures, toexcise them or parts of them, to treat them, etc., can require cuttingthrough, dissecting and/or harming numerous muscles and muscle groups inor about, without limitation, the skull or face, the abdomen, the ribsand/or the thoracic cavity, as well as in or about all joints andappendages. Prehabilitation can improve a patient's speed of recovery,measure of quality of life, level of pain, etc. in all the foregoingprocedures. In one embodiment of prehabilitation, a pre-surgicalprocedure or a pre-non-surgical-treatment may include one or more setsof exercises for a patient to perform prior to such procedure ortreatment. Performance of the one or more sets of exercises may berequired in order to qualify for an elective surgery, such as a kneereplacement. The patient may prepare an area of his or her body for thesurgical procedure by performing the one or more sets of exercises,thereby strengthening muscle groups, improving existing muscle memory,reducing pain, reducing stiffness, establishing new muscle memory,enhancing mobility (i.e., improve range of motion), improving bloodflow, and/or the like.

The phrase, and all permutations of the phrase, “respective measure ofbenefit with which one or more exercise regimens may provide the user”(e.g., “measure of benefit,” “respective measures of benefit,” “measuresof benefit,” “measure of exercise regimen benefit,” “exercise regimenbenefit measurement,” etc.) may refer to one or more measures of benefitwith which one or more exercise regimens may provide the user.

DETAILED DESCRIPTION

The following discussion is directed to various embodiments of thepresent disclosure. Although one or more of these embodiments may bepreferred, the embodiments disclosed should not be interpreted, orotherwise used, as limiting the scope of the disclosure, including theclaims. In addition, one skilled in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

The following discussion is directed to various embodiments of thepresent disclosure. Although one or more of these embodiments may bepreferred, the embodiments disclosed should not be interpreted, orotherwise used, as limiting the scope of the disclosure, including theclaims. In addition, one skilled in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

Determining a treatment plan for a patient having certaincharacteristics (e.g., vital-sign or other measurements; performance;demographic; psychographic; geographic; diagnostic; measurement- ortest-based; medically historic; behavioral historic; cognitive;etiologic; cohort-associative; differentially diagnostic; surgical,physically therapeutic, microbiome related, pharmacologic and othertreatments recommended; arterial blood gas and/or oxygenation levels orpercentages; glucose levels; blood oxygen levels; insulin levels;psychographics; etc.) may be a technically challenging problem. Forexample, a multitude of information may be considered when determining atreatment plan, which may result in inefficiencies and inaccuracies inthe treatment plan selection process. In a rehabilitative setting, someof the multitude of information considered may include characteristicsof the patient such as personal information, performance information,and measurement information. The personal information may include, e.g.,demographic, psychographic or other information, such as an age, aweight, a gender, a height, a body mass index, a medical condition, afamilial medication history, an injury, a medical procedure, amedication prescribed, or some combination thereof. The performanceinformation may include, e.g., an elapsed time of using a treatmentapparatus, an amount of force exerted on a portion of the treatmentapparatus, a range of motion achieved on the treatment apparatus, amovement speed of a portion of the treatment apparatus, a duration ofuse of the treatment apparatus, an indication of a plurality of painlevels using the treatment apparatus, or some combination thereof. Themeasurement information may include, e.g., a vital sign, a respirationrate, a heartrate, a temperature, a blood pressure, a glucose level,arterial blood gas and/or oxygenation levels or percentages, or otherbiomarker, or some combination thereof. It may be desirable to processand analyze the characteristics of a multitude of patients, thetreatment plans performed for those patients, and the results of thetreatment plans for those patients.

Further, another technical problem may involve distally treating, via acomputing apparatus during a telemedicine session, a patient from alocation different than a location at which the patient is located. Anadditional technical problem is controlling or enabling, from thedifferent location, the control of a treatment apparatus used by thepatient at the patient's location. Oftentimes, when a patient undergoesrehabilitative surgery (e.g., knee surgery), a healthcare professionalmay prescribe a treatment apparatus to the patient to use to perform atreatment protocol at their residence or at any mobile location ortemporary domicile. A healthcare professional may refer to a doctor,physician assistant, nurse practitioner, nurse, chiropractor, dentist,physical therapist, acupuncturist, physical trainer, or the like. Ahealthcare professional may refer to any person with a credential,license, degree, or the like in the field of medicine, physical therapy,rehabilitation, or the like.

When the healthcare professional is located in a different location fromthe patient and the treatment apparatus, it may be technicallychallenging for the healthcare professional to monitor the patient'sactual progress (as opposed to relying on the patient's word about theirprogress) in using the treatment apparatus, modify the treatment planaccording to the patient's progress, adapt the treatment apparatus tothe personal characteristics of the patient as the patient performs thetreatment plan, and the like.

Additionally, or alternatively, a computer-implemented system may beused in connection with a treatment apparatus to treat the patient, forexample, during a telemedicine session. For example, the treatmentapparatus can be configured to be manipulated by a user while the useris performing a treatment plan. The system may include a patientinterface that includes an output device configured to presenttelemedicine information associated with the telemedicine session.During the telemedicine session, the processing device can be configuredto receive treatment data pertaining to the user. The treatment data mayinclude one or more characteristics of the user. The processing devicemay be configured to determine, via one or more trained machine learningmodels, at least one respective measure of benefit which one or moreexercise regimens provide the user. Determining the respective measureof benefit may be based on the treatment data. The processing device maybe configured to determine, via the one or more trained machine learningmodels, one or more probabilities of the user complying with the one ormore exercise regimens. The processing device may be configured totransmit the treatment plan, for example, to a computing device. Thetreatment plan can be generated based on the one or more probabilitiesand the respective measure of benefit which the one or more exerciseregimens provide the user.

Accordingly, systems and methods, such as those described herein, thatreceive treatment data pertaining to the user of the treatment apparatusduring telemedicine session, may be desirable.

In some embodiments, the systems and methods described herein may beconfigured to use a treatment apparatus configured to be manipulated byan individual while performing a treatment plan. The individual mayinclude a user, patient, or other a person using the treatment apparatusto perform various exercises for prehabilitation, rehabilitation,stretch training, and the like. The systems and methods described hereinmay be configured to use and/or provide a patient interface comprisingan output device configured to present telemedicine informationassociated with a telemedicine session.

In some embodiments, during an adaptive telemedicine session, thesystems and methods described herein may be configured to use artificialintelligence and/or machine learning to assign patients to cohorts andto dynamically control a treatment apparatus based on the assignment.The term “adaptive telemedicine” may refer to a telemedicine sessiondynamically adapted based on one or more factors, criteria, parameters,characteristics, or the like. The one or more factors, criteria,parameters, characteristics, or the like may pertain to the user (e.g.,heartrate, blood pressure, perspiration rate, pain level, or the like),the treatment apparatus (e.g., pressure, range of motion, speed ofmotor, etc.), details of the treatment plan, and so forth.

In some embodiments, numerous patients may be prescribed numeroustreatment apparatuses because the numerous patients are recovering fromthe same medical procedure and/or suffering from the same injury. Thenumerous treatment apparatuses may be provided to the numerous patients.The treatment apparatuses may be used by the patients to performtreatment plans in their residences, at gyms, at rehabilitative centers,at hospitals, or at any suitable locations, including permanent ortemporary domiciles.

In some embodiments, the treatment apparatuses may be communicativelycoupled to a server. Characteristics of the patients, including thetreatment data, may be collected before, during, and/or after thepatients perform the treatment plans. For example, any or each of thepersonal information, the performance information, and the measurementinformation may be collected before, during, and/or after a patientperforms the treatment plans. The results (e.g., improved performance ordecreased performance) of performing each exercise may be collected fromthe treatment apparatus throughout the treatment plan and after thetreatment plan is performed. The parameters, settings, configurations,etc. (e.g., position of pedal, amount of resistance, etc.) of thetreatment apparatus may be collected before, during, and/or after thetreatment plan is performed.

Each characteristic of the patient, each result, and each parameter,setting, configuration, etc. may be timestamped and may be correlatedwith a particular step or set of steps in the treatment plan. Such atechnique may enable the determination of which steps in the treatmentplan lead to desired results (e.g., improved muscle strength, range ofmotion, etc.) and which steps lead to diminishing returns (e.g.,continuing to exercise after 3 minutes actually delays or harmsrecovery).

Data may be collected from the treatment apparatuses and/or any suitablecomputing device (e.g., computing devices where personal information isentered, such as the interface of the computing device described herein,a clinician interface, patient interface, or the like) over time as thepatients use the treatment apparatuses to perform the various treatmentplans. The data that may be collected may include the characteristics ofthe patients, the treatment plans performed by the patients, and theresults of the treatment plans. Further, the data may includecharacteristics of the treatment apparatus. The characteristics of thetreatment apparatus may include a make (e.g., identity of entity thatdesigned, manufactured, etc. a treatment apparatus 70) of the treatmentapparatus 70, a model (e.g., model number or other identifier of themodel) of the treatment apparatus 70, a year (e.g., year the treatmentapparatus was manufactured) of the treatment apparatus 70, operationalparameters (e.g., engine temperature during operation, a respectivestatus of each of one or more sensors included in or associated with thetreatment apparatus 70, vibration measurements of the treatmentapparatus 70 in operation, measurements of static and/or dynamic forcesexerted internally or externally on the treatment apparatus 70, etc.) ofthe treatment apparatus 70, settings (e.g., range of motion setting,speed setting, required pedal force setting, etc.) of the treatmentapparatus 70, and the like. The data collected from the treatmentapparatuses, computing devices, characteristics of the user,characteristics of the treatment apparatus, and the like may becollectively referred to as “treatment data” herein.

In some embodiments, the data may be processed to group certain peopleinto cohorts. The people may be grouped by people having certain orselected similar characteristics, treatment plans, and results ofperforming the treatment plans. For example, athletic people having nomedical conditions who perform a treatment plan (e.g., use the treatmentapparatus for 30 minutes a day 5 times a week for 3 weeks) and who fullyrecover may be grouped into a first cohort. Older people who areclassified obese and who perform a treatment plan (e.g., use thetreatment plan for 10 minutes a day 3 times a week for 4 weeks) and whoimprove their range of motion by 75 percent may be grouped into a secondcohort.

In some embodiments, an artificial intelligence engine may include oneor more machine learning models that are trained using the cohorts. Insome embodiments, the artificial intelligence engine may be used toidentify trends and/or patterns and to define new cohorts based onachieving desired results from the treatment plans and machine learningmodels associated therewith may be trained to identify such trendsand/or patterns and to recommend and rank the desirability of the newcohorts. For example, the one or more machine learning models may betrained to receive an input of characteristics of a new patient and tooutput a treatment plan for the patient that results in a desiredresult. The machine learning models may match a pattern between thecharacteristics of the new patient and at least one patient of thepatients included in a particular cohort. When a pattern is matched, themachine learning models may assign the new patient to the particularcohort and select the treatment plan associated with the at least onepatient. The artificial intelligence engine may be configured tocontrol, distally and based on the treatment plan, the treatmentapparatus while the new patient uses the treatment apparatus to performthe treatment plan.

As may be appreciated, the characteristics of the new patient (e.g., anew user) may change as the new patient uses the treatment apparatus toperform the treatment plan. For example, the performance of the patientmay improve quicker than expected for people in the cohort to which thenew patient is currently assigned. Accordingly, the machine learningmodels may be trained to dynamically reassign, based on the changedcharacteristics, the new patient to a different cohort that includespeople having characteristics similar to the now-changed characteristicsas the new patient. For example, a clinically obese patient may loseweight and no longer meet the weight criterion for the initial cohort,result in the patient's being reassigned to a different cohort with adifferent weight criterion.

A different treatment plan may be selected for the new patient, and thetreatment apparatus may be controlled, distally (e.g., which may bereferred to as remotely) and based on the different treatment plan, thetreatment apparatus while the new patient uses the treatment apparatusto perform the treatment plan. Such techniques may provide the technicalsolution of distally controlling a treatment apparatus.

Further, the systems and methods described herein may lead to fasterrecovery times and/or better results for the patients because thetreatment plan that most accurately fits their characteristics isselected and implemented, in real-time, at any given moment. “Real-time”may also refer to near real-time, which may be less than 10 seconds orany reasonably proximate difference between two different times. Asdescribed herein, the term “results” may refer to medical results ormedical outcomes. Results and outcomes may refer to responses to medicalactions. The term “medical action(s)” may refer to any suitable actionperformed by the healthcare professional, and such action or actions mayinclude diagnoses, prescription of treatment plans, prescription oftreatment apparatuses, and the making, composing and/or executing ofappointments, telemedicine sessions, prescription of medicines,telephone calls, emails, text messages, and the like.

Depending on what result is desired, the artificial intelligence enginemay be trained to output several treatment plans. For example, oneresult may include recovering to a threshold level (e.g., 75% range ofmotion) in a fastest amount of time, while another result may includefully recovering (e.g., 100% range of motion) regardless of the amountof time. The data obtained from the patients and sorted into cohorts mayindicate that a first treatment plan provides the first result forpeople with characteristics similar to the patient's, and that a secondtreatment plan provides the second result for people withcharacteristics similar to the patient.

Further, the artificial intelligence engine may be trained to outputtreatment plans that are not optimal i.e., sub-optimal, nonstandard, orotherwise excluded (all referred to, without limitation, as “excludedtreatment plans”) for the patient. For example, if a patient has highblood pressure, a particular exercise may not be approved or suitablefor the patient as it may put the patient at unnecessary risk or eveninduce a hypertensive crisis and, accordingly, that exercise may beflagged in the excluded treatment plan for the patient. In someembodiments, the artificial intelligence engine may monitor thetreatment data received while the patient (e.g., the user) with, forexample, high blood pressure, uses the treatment apparatus to perform anappropriate treatment plan and may modify the appropriate treatment planto include features of an excluded treatment plan that may providebeneficial results for the patient if the treatment data indicates thepatient is handling the appropriate treatment plan without aggravating,for example, the high blood pressure condition of the patient. In someembodiments, the artificial intelligence engine may modify the treatmentplan if the monitored data shows the plan to be inappropriate orcounterproductive for the user.

In some embodiments, the treatment plans and/or excluded treatment plansmay be presented, during a telemedicine or telehealth session, to ahealthcare professional. The healthcare professional may select aparticular treatment plan for the patient to cause that treatment planto be transmitted to the patient and/or to control, based on thetreatment plan, the treatment apparatus. In some embodiments, tofacilitate telehealth or telemedicine applications, including remotediagnoses, determination of treatment plans and rehabilitative and/orpharmacologic prescriptions, the artificial intelligence engine mayreceive and/or operate distally from the patient and the treatmentapparatus.

In such cases, the recommended treatment plans and/or excluded treatmentplans may be presented simultaneously with a video of the patient inreal-time or near real-time during a telemedicine or telehealth sessionon a user interface of a computing apparatus of a healthcareprofessional. The video may also be accompanied by audio, text and othermultimedia information and/or other sensorial or perceptive (e.g.,tactile, gustatory, haptic, pressure-sensing-based or electromagnetic(e.g., neurostimulation). Real-time may refer to less than or equal to 2seconds. Near real-time may refer to any interaction of a sufficientlyshort time to enable two individuals to engage in a dialogue via suchuser interface, and will generally be less than 10 seconds (or anysuitably proximate difference or interval between two different times)but greater than 2 seconds. Presenting the treatment plans generated bythe artificial intelligence engine concurrently with a presentation ofthe patient video may provide an enhanced user interface because thehealthcare professional may continue to visually and/or otherwisecommunicate with the patient while also reviewing the treatment plans onthe same user interface. The enhanced user interface may improve thehealthcare professional's experience using the computing device and mayencourage the healthcare professional to reuse the user interface. Sucha technique may also reduce computing resources (e.g., processing,memory, network) because the healthcare professional does not have toswitch to another user interface screen to enter a query for a treatmentplan to recommend based on the characteristics of the patient. Theartificial intelligence engine may be configured to provide, dynamicallyon the fly, the treatment plans and excluded treatment plans.

In some embodiments, the treatment plan may be modified by a healthcareprofessional. For example, certain procedures may be added, modified orremoved. In the telehealth scenario, there are certain procedures thatmay not be performed due to the distal nature of a healthcareprofessional using a computing device in a different physical locationthan a patient.

A technical problem may relate to the information pertaining to thepatient's medical condition being received in disparate formats. Forexample, a server may receive the information pertaining to a medicalcondition of the patient from one or more sources (e.g., from anelectronic medical record (EMR) system, application programminginterface (API), or any suitable system that has information pertainingto the medical condition of the patient). That is, some sources used byvarious healthcare professional entities may be installed on their localcomputing devices and, additionally and/or alternatively, may useproprietary formats. Accordingly, some embodiments of the presentdisclosure may use an API to obtain, via interfaces exposed by APIs usedby the sources, the formats used by the sources. In some embodiments,when information is received from the sources, the API may map andconvert the format used by the sources to a standardized (i.e.,canonical) format, language and/or encoding (“format” as used hereinwill be inclusive of all of these terms) used by the artificialintelligence engine. Further, the information converted to thestandardized format used by the artificial intelligence engine may bestored in a database accessed by the artificial intelligence engine whenthe artificial intelligence engine is performing any of the techniquesdisclosed herein. Using the information converted to a standardizedformat may enable a more accurate determination of the procedures toperform for the patient.

The various embodiments disclosed herein may provide a technicalsolution to the technical problem pertaining to the patient's medicalcondition information being received in disparate formats. For example,a server may receive the information pertaining to a medical conditionof the patient from one or more sources (e.g., from an electronicmedical record (EMR) system, application programming interface (API), orany suitable system that has information pertaining to the medicalcondition of the patient). The information may be converted from theformat used by the sources to the standardized format used by theartificial intelligence engine. Further, the information converted tothe standardized format used by the artificial intelligence engine maybe stored in a database accessed by the artificial intelligence enginewhen performing any of the techniques disclosed herein. The standardizedinformation may enable generating optimal treatment plans, where thegenerating is based on treatment plans associated with the standardizedinformation. The optimal treatment plans may be provided in astandardized format that can be processed by various applications (e.g.,telehealth) executing on various computing devices of healthcareprofessionals and/or patients.

A technical problem may include a challenge of generating treatmentplans for users, such treatment plans comprising exercises that balancea measure of benefit which the exercise regimens provide to the user andthe probability the user complies with the exercises (or the distinctprobabilities the user complies with each of the one or more exercises).By selecting exercises having higher compliance probabilities for theuser, more efficient treatment plans may be generated, and these mayenable less frequent use of the treatment apparatus and therefore extendthe lifetime or time between recommended maintenance of or neededrepairs to the treatment apparatus. For example, if the userconsistently quits a certain exercise but yet attempts to perform theexercise multiple times thereafter, the treatment apparatus may be usedmore times, and therefore suffer more “wear-and-tear” than if the userfully complies with the exercise regimen the first time. In someembodiments, a technical solution may include using trained machinelearning models to generate treatment plans based on the measure ofbenefit exercise regimens provide users and the probabilities of theusers associated with complying with the exercise regimens, suchinclusion thereby leading to more time-efficient, cost-efficient, andmaintenance-efficient use of the treatment apparatus.

In some embodiments, the treatment apparatus may be adaptive and/orpersonalized because its properties, configurations, and positions maybe adapted to the needs of a particular patient. For example, the pedalsmay be dynamically adjusted on the fly (e.g., via a telemedicine sessionor based on programmed configurations in response to certainmeasurements being detected) to increase or decrease a range of motionto comply with a treatment plan designed for the user. In someembodiments, a healthcare professional may adapt, remotely during atelemedicine session, the treatment apparatus to the needs of thepatient by causing a control instruction to be transmitted from a serverto treatment apparatus.

Such adaptive nature may improve the results of recovery for a patient,furthering the goals of personalized medicine, and enablingpersonalization of the treatment plan on a per-individual basis.

Center-based rehabilitation may be prescribed for certain patients thatqualify and/or are eligible for cardiac rehabilitation. Further, the useof exercise equipment to stimulate blood flow and heart health may bebeneficial for a plethora of other rehabilitation, in addition tocardiac rehabilitation, such as pulmonary rehabilitation, bariatricrehabilitation, cardio-oncologic rehabilitation, orthopedicrehabilitation, any other type of rehabilitation. However, center-basedrehabilitation suffers from many disadvantages. For example,center-based access requires the patient to travel from their place ofresidence to the center to use the rehabilitation equipment. Travelingis a barrier to entry for some because not all people have vehicles ordesire to spend money on gas to travel to a center. Further,center-based rehabilitation programs may not be individually tailored toa patient. That is, the center-based rehabilitation program may beone-size fits all based on a type of medical condition the patientunderwent. In addition, center-based rehabilitation require the patientto adhere to a schedule of when the center is open, when therehabilitation equipment is available, when the support staff isavailable, etc. In addition, center-based rehabilitation, due to thefact the rehabilitation is performed in a public center, lacks privacy.Center-based rehabilitation also suffers from weather constraints inthat detrimental weather may prevent a patient from traveling to thecenter to comply with their rehabilitation program.

Accordingly, home-based rehabilitation may solve one or more of theissues related to center-based rehabilitation and provide variousadvantages over center-based rehabilitation. For example, home-basedrehabilitation may require decreased days to enrollment, provide greateraccess for patients to engage in the rehabilitation, and provideindividually tailored treatment plans based on one or morecharacteristics of the patient. Further, home-based rehabilitationprovides greater flexibility in scheduling, as the rehabilitation may beperformed at any time during the day when the user is at home anddesires to perform the treatment plan. There is no transportationbarrier for home-based rehabilitation since the treatment apparatus islocated within the user's residence. Home-based rehabilitation providesgreater privacy for the patient because the patient is performing thetreatment plan within their own residence. To that end, the treatmentplan implementing the rehabilitation may be easily integrated in to thepatient's home routine. The home-based rehabilitation may be provided tomore patients than center-based rehabilitation because the treatmentapparatus may be delivered to rural regions. Additionally, home-basedrehabilitation does not suffer from weather concerns.

This disclosure may refer, inter alia, to “cardiac conditions,”“cardiac-related events” (also called “CREs” or “cardiac events”),“cardiac interventions” and “cardiac outcomes.”

“Cardiac conditions,” as used herein, may refer to medical, health orother characteristics or attributes associated with a cardiological orcardiovascular state. Cardiac conditions are descriptions, measurements,diagnoses, etc. which refer or relate to a state, attribute orexplanation of a state pertaining to the cardiovascular system. Forexample, if one's heart is beating too fast for a given context, thenthe cardiac condition describing that is “tachycardia”; if one has hadthe left mitral valve of the heart replaced, then the cardiac conditionis that of having a replaced mitral valve. If one has suffered amyocardial infarction, that term, too, is descriptive of a cardiaccondition. A distinguishing point is that a cardiac condition reflects astate of a patient's cardiovascular system at a given point in time. Itis, however, not an event or occurrence itself. Much as a needle canprick a balloon and burst the balloon, deflating it, the state orcondition of the balloon is that it has been burst, while the eventwhich caused that is entirely different, i.e., the needle pricking theballoon. Without limiting the foregoing, a cardiac condition may referto an already existing cardiac condition, a change in state (e.g., anexacerbation or worsening) in or to an existing cardiac condition,and/or an appearance of a new cardiac condition. One or more cardiacconditions of a user may be used to describe the cardiac health of theuser.

A “cardiac event,” “cardiac-related event” or “CRE,” on the other hand,is something that has occurred with respect to one's cardiovascularsystem and it may be a contributing, associated or precipitating causeof one or more cardiac conditions, but it is the causative reason forthe one or more cardiac conditions or a contributing or associatedreason for the one or more cardiac conditions. For example, if anangioplasty procedure results in a rupture of a blood vessel in theheart, the rupture is the CRE, while the underlying condition thatcaused the angioplasty to fail was the cardiac condition of having ananeurysm. The aneurysm is a cardiac condition, not a CRE. The rupture isthe CRE. The angioplasty is the cause of the CRE (the rupture), but isnot a cardiac condition (a heart cannot be “angioplastic”). Theangioplasty procedure can also be deemed a CRE in and of itself, becauseit is an active, dynamic process, not a description of a state.

For example, and without limiting the foregoing, CREs may includecardiac-related medical conditions and events, and may also be aconsequence of procedures or interventions (including, withoutlimitation, cardiac interventions, as defined infra) that may negativelyaffect the health, performance, or predicted future performance of thecardiovascular system or of any physiological systems or health-relatedattributes of a patient where such systems or attributes are themselvesaffected by the performance of the patient's cardiovascular system.These CREs may render individuals, optionally with extant comorbidities,susceptible to a first comorbidity or additional comorbidities orindependent medical problems such as, without limitation, congestiveheart failure, fatigue issues, oxygenation issues, pulmonary issues,vascular issues, cardio-renal anemia syndrome (CRAS), muscle lossissues, endurance issues, strength issues, sexual performance issues(such as erectile dysfunction), ambulatory issues, obesity issues,reduction of lifespan issues, reduction of quality-of-life issues, andthe like. “Issues,” as used in the foregoing, may refer, withoutlimitation, to exacerbations, reductions, mitigations, compromisedfunctionings, eliminations, or other directly or indirectly causedchanges in an underlying condition or physiological organ orpsychological characteristic of the individual or the sequelae of anysuch change, where the existence of at least one said issue may resultin a diminution of the quality of life for the individual. The existenceof such an at least one issue may itself be remediated by reversing,mitigating, controlling, or otherwise ameliorating the effects of saidexacerbations, reductions, mitigations, compromised functionings,eliminations, or other directly or indirectly caused changes in anunderlying condition or physiological organ or psychologicalcharacteristic of the individual or the sequelae of such change. Ingeneral, when an individual suffers a CRE, the individual's overallquality of life may become substantially degraded, compared to its priorstate.

A “cardiac intervention” is a process, procedure, surgery, drug regimenor other medical intervention or action undertaken with the intent tominimize the negative effects of a CRE (or, if a CRE were to havepositive effects, to maximize those positive effects) that has alreadyoccurred, that is about to occur or that is predicted to occur with someprobability greater than zero, or to eliminate the negative effectsaltogether. A cardiac intervention may also be undertaken before a CREoccurs with the intent to avoid the CRE from occurring or to mitigatethe negative consequences of the CRE should the CRE still occur.

A “cardiac outcome” may be the result of either a cardiac interventionor other treatment or the result of a CRE for which no cardiacintervention or other treatment has been performed. For example, if apatient dies from the CRE of a ruptured aorta due to the cardiaccondition of an aneurysm, and the death occurs because of, in spite of,or without any cardiac interventions, then the cardiac outcome is thepatient's death. On the other hand, if a patient has the cardiacconditions of atherosclerosis, hypertension, and dyspnea, and thecardiac intervention of a balloon angioplasty is performed to insert astent to reduce the effects of arterial stenosis (another cardiaccondition), then the cardiac outcome can be significantly improvedcardiac health for the patient. Accordingly, a cardiac outcome maygenerally refer, in some examples, to both negative and positiveoutcomes.

To use an analogy of an automobile, an automotive condition may be dirtyoil. If the oil is not changed, it may damage the engine. The enginedamage is an automotive condition, but the time when the engine sustainsdamage due to the particulate matter in the oil is an“automotive-related event,” the analogue to a CRE. If an automotiveintervention is undertaken, the oil will be changed before it can damagethe engine; or, if the engine has already been damaged, then anautomotive intervention involving specific repairs to the engine will beundertaken. If ultimately the engine fails to work, then the automotiveoutcome is a broken engine; on the other hand, if the automotiveinterventions succeed, then the automotive outcome is that theautomobile's performance is brought back to a factory-standard orfactory-acceptable level.

Despite the multifarious problems arising out of the foregoingquality-of-life issues, research has shown that exercise rehabilitationprograms can substantially mitigate or ameliorate said issues as well asimprove each affected individual's quality of life. In particular, suchprograms enable these improvements by enhancing aerobic exercisepotential, increasing coronary perfusion, and decreasing both anxietyand depression (which, inter alia, may be present in patients sufferingCREs). Moreover, participation in cardiac rehabilitation has resulted indemonstrated reductions in re-hospitalizations, in progressions ofcoronary vascular disease, and in negative cardiac outcomes (e.g.,death). Systems and methods implementing the principles of the presentdisclosure as described below in more detail are configured to reducethe probability that an individual will a cardiac intervention.

FIG. 1 shows a block diagram of a computer-implemented system 10,hereinafter called “the system” for managing a treatment plan. Managingthe treatment plan may include using an artificial intelligence engineto recommend treatment plans and/or provide excluded treatment plansthat should not be recommended to a patient.

The system 10 also includes a server 30 configured to store and toprovide data related to managing the treatment plan. The server 30 mayinclude one or more computers and may take the form of a distributedand/or virtualized computer or computers. The server 30 also includes afirst communication interface 32 configured to communicate with theclinician interface 20 via a first network 34. In some embodiments, thefirst network 34 may include wired and/or wireless network connectionssuch as Wi-Fi, Bluetooth, ZigBee, Near-Field Communications (NFC),cellular data network, etc. The server 30 includes a first processor 36and a first machine-readable storage memory 38, which may be called a“memory” for short, holding first instructions 40 for performing thevarious actions of the server 30 for execution by the first processor36. The server 30 is configured to store data regarding the treatmentplan. For example, the memory 38 includes a system data store 42configured to hold system data, such as data pertaining to treatmentplans for treating one or more patients.

The system data store 42 may be configured to store optimal treatmentplans generated based on one or more probabilities of users associatedwith complying with the exercise regimens, and the measure of benefitwith which one or more exercise regimens provide the user. The systemdata store 42 may hold data pertaining to one or more exercises (e.g., atype of exercise, which body part the exercise affects, a duration ofthe exercise, which treatment apparatus to use to perform the exercise,repetitions of the exercise to perform, etc.). When any of thetechniques described herein are being performed, or prior to orthereafter such performance, any of the data stored in the system datastore 42 may be accessed by an artificial intelligence engine 11.

The server 30 may also be configured to store data regarding performanceby a patient in following a treatment plan. For example, the memory 38includes a patient data store 44 configured to hold patient data, suchas data pertaining to the one or more patients, including datarepresenting each patient's performance within the treatment plan. Thepatient data store 44 may hold treatment data pertaining to users overtime, such that historical treatment data is accumulated in the patientdata store 44. The patient data store 44 may hold data pertaining tomeasures of benefit one or more exercises provide to users,probabilities of the users complying with the exercise regimens, and thelike. The exercise regimens may include any suitable number of exercises(e.g., shoulder raises, squats, cardiovascular exercises, sit-ups,curls, etc.) to be performed by the user. When any of the techniquesdescribed herein are being performed, or prior to or thereafter suchperformance, any of the data stored in the patient data store 44 may beaccessed by an artificial intelligence engine 11.

In addition, the determination or identification of: the characteristics(e.g., personal, performance, measurement, etc.) of the users, thetreatment plans followed by the users, the measure of benefits whichexercise regimens provide to the users, the probabilities of the usersassociated with complying with exercise regimens, the level ofcompliance with the treatment plans (e.g., the user completed 4 out of 5exercises in the treatment plans, the user completed 80% of an exercisein the treatment plan, etc.), and the results of the treatment plans mayuse correlations and other statistical or probabilistic measures toenable the partitioning of or to partition the treatment plans intodifferent patient cohort-equivalent databases in the patient data store44. For example, the data for a first cohort of first patients having afirst determined measure of benefit provided by exercise regimens, afirst determined probability of the user associated with complying withexercise regimens, a first similar injury, a first similar medicalcondition, a first similar medical procedure performed, a firsttreatment plan followed by the first patient, and/or a first result ofthe treatment plan, may be stored in a first patient database. The datafor a second cohort of second patients having a second determinedmeasure of benefit provided by exercise regimens, a second determinedprobability of the user associated with complying with exerciseregimens, a second similar injury, a second similar medical condition, asecond similar medical procedure performed, a second treatment planfollowed by the second patient, and/or a second result of the treatmentplan may be stored in a second patient database. Any singlecharacteristic, any combination of characteristics, or any measurescalculation therefrom or thereupon may be used to separate the patientsinto cohorts. In some embodiments, the different cohorts of patients maybe stored in different partitions or volumes of the same database. Thereis no specific limit to the number of different cohorts of patientsallowed, other than as limited by mathematical combinatoric and/orpartition theory.

This measure of exercise benefit data, user compliance probability data,characteristic data, treatment plan data, and results data may beobtained from numerous treatment apparatuses and/or computing devicesover time and stored in the database 44. The measure of exercise benefitdata, user compliance probability data, characteristic data, treatmentplan data, and results data may be correlated in the patient-cohortdatabases in the patient data store 44. The characteristics of the usersmay include personal information, performance information, and/ormeasurement information.

In addition to the historical treatment data, measure of exercisebenefit data, and/or user compliance probability data about other usersstored in the patient cohort-equivalent databases, real-time ornear-real-time information based on the current patient's treatmentdata, measure of exercise benefit data, and/or user complianceprobability data about a current patient being treated may be stored inan appropriate patient cohort-equivalent database. The treatment data,measure of exercise benefit data, and/or user compliance probabilitydata of the patient may be determined to match or be similar to thetreatment data, measure of exercise benefit data, and/or user complianceprobability data of another person in a particular cohort (e.g., a firstcohort “A”, a second cohort “B” or a third cohort “C”, etc.) and thepatient may be assigned to the selected or associated cohort.

In some embodiments, the server 30 may execute the artificialintelligence (AI) engine 11 that uses one or more machine learningmodels 13 to perform at least one of the embodiments disclosed herein.The server 30 may include a training engine 9 capable of generating theone or more machine learning models 13. The machine learning models 13may be trained to assign users to certain cohorts based on theirtreatment data, generate treatment plans using real-time and historicaldata correlations involving patient cohort-equivalents, and control atreatment apparatus 70, among other things. The machine learning models13 may be trained to generate, based on one or more probabilities of theuser complying with one or more exercise regimens and/or a respectivemeasure of benefit one or more exercise regimens provide the user, atreatment plan at least a subset of the one or more exercises for theuser to perform. The one or more machine learning models 13 may begenerated by the training engine 9 and may be implemented in computerinstructions executable by one or more processing devices of thetraining engine 9 and/or the servers 30. To generate the one or moremachine learning models 13, the training engine 9 may train the one ormore machine learning models 13. The one or more machine learning models13 may be used by the artificial intelligence engine 11.

The training engine 9 may be a rackmount server, a router computer, apersonal computer, a portable digital assistant, a smartphone, a laptopcomputer, a tablet computer, a netbook, a desktop computer, an Internetof Things (IoT) device, any other desired computing device, or anycombination of the above. The training engine 9 may be cloud-based or areal-time software platform, and it may include privacy software orprotocols, and/or security software or protocols.

To train the one or more machine learning models 13, the training engine9 may use a training data set of a corpus of information (e.g.,treatment data, measures of benefits of exercises provide to users,probabilities of users complying with the one or more exercise regimens,etc.) pertaining to users who performed treatment plans using thetreatment apparatus 70, the details (e.g., treatment protocol includingexercises, amount of time to perform the exercises, instructions for thepatient to follow, how often to perform the exercises, a schedule ofexercises, parameters/configurations/settings of the treatment apparatus70 throughout each step of the treatment plan, etc.) of the treatmentplans performed by the users using the treatment apparatus 70, and/orthe results of the treatment plans performed by the users, etc.

The one or more machine learning models 13 may be trained to matchpatterns of treatment data of a user with treatment data of other usersassigned to a particular cohort. The term “match” may refer to an exactmatch, a correlative match, a substantial match, a probabilistic match,etc. The one or more machine learning models 13 may be trained toreceive the treatment data of a patient as input, map the treatment datato the treatment data of users assigned to a cohort, and determine arespective measure of benefit one or more exercise regimens provide tothe user based on the measures of benefit the exercises provided to theusers assigned to the cohort. The one or more machine learning models 13may be trained to receive the treatment data of a patient as input, mapthe treatment data to treatment data of users assigned to a cohort, anddetermine one or more probabilities of the user associated withcomplying with the one or more exercise regimens based on theprobabilities of the users in the cohort associated with complying withthe one or more exercise regimens. The one or more machine learningmodels 13 may also be trained to receive various input (e.g., therespective measure of benefit which one or more exercise regimensprovide the user; the one or more probabilities of the user complyingwith the one or more exercise regimens; an amount, quality or othermeasure of sleep associated with the user; information pertaining to adiet of the user, information pertaining to an eating schedule of theuser; information pertaining to an age of the user, informationpertaining to a sex of the user; information pertaining to a gender ofthe user; an indication of a mental state of the user; informationpertaining to a genetic condition of the user; information pertaining toa disease state of the user; an indication of an energy level of theuser; or some combination thereof), and to output a generated treatmentplan for the patient.

The one or more machine learning models 13 may be trained to matchpatterns of a first set of parameters (e.g., treatment data, measures ofbenefits of exercises provided to users, probabilities of usercompliance associated with the exercises, etc.) with a second set ofparameters associated with an optimal treatment plan. The one or moremachine learning models 13 may be trained to receive the first set ofparameters as input, map the characteristics to the second set ofparameters associated with the optimal treatment plan, and select theoptimal treatment plan. The one or more machine learning models 13 mayalso be trained to control, based on the treatment plan, the treatmentapparatus 70.

Using training data that includes training inputs and correspondingtarget outputs, the one or more machine learning models 13 may refer tomodel artifacts created by the training engine 9. The training engine 9may find patterns in the training data wherein such patterns map thetraining input to the target output, and generate the machine learningmodels 13 that capture these patterns. In some embodiments, theartificial intelligence engine 11, the database 33, and/or the trainingengine 9 may reside on another component (e.g., assistant interface 94,clinician interface 20, etc.) depicted in FIG. 1 .

The one or more machine learning models 13 may comprise, e.g., a singlelevel of linear or non-linear operations (e.g., a support vector machine[SVM]) or the machine learning models 13 may be a deep network, i.e., amachine learning model comprising multiple levels of non-linearoperations. Examples of deep networks are neural networks includinggenerative adversarial networks, convolutional neural networks,recurrent neural networks with one or more hidden layers, and fullyconnected neural networks (e.g., each neuron may transmit its outputsignal to the input of the remaining neurons, as well as to itself). Forexample, the machine learning model may include numerous layers and/orhidden layers that perform calculations (e.g., dot products) usingvarious neurons.

Further, in some embodiments, based on subsequent data (e.g., treatmentdata, measures of exercise benefit data, probabilities of usercompliance data, treatment plan result data, etc.) received, the machinelearning models 13 may be continuously or continually updated. Forexample, the machine learning models 13 may include one or more hiddenlayers, weights, nodes, parameters, and the like. As the subsequent datais received, the machine learning models 13 may be updated such that theone or more hidden layers, weights, nodes, parameters, and the like areupdated to match or be computable from patterns found in the subsequentdata. Accordingly, the machine learning models 13 may be re-trained onthe fly as subsequent data is received, and therefore, the machinelearning models 13 may continue to learn.

The system 10 also includes a patient interface 50 configured tocommunicate information to a patient and to receive feedback from thepatient. Specifically, the patient interface includes an input device 52and an output device 54, which may be collectively called a patient userinterface 52, 54. The input device 52 may include one or more devices,such as a keyboard, a mouse, a touch screen input, a gesture sensor,and/or a microphone and processor configured for voice recognition. Theoutput device 54 may take one or more different forms including, forexample, a computer monitor or display screen on a tablet, smartphone,or a smart watch. The output device 54 may include other hardware and/orsoftware components such as a projector, virtual reality capability,augmented reality capability, etc. The output device 54 may incorporatevarious different visual, audio, or other presentation technologies. Forexample, the output device 54 may include a non-visual display, such asan audio signal, which may include spoken language and/or other soundssuch as tones, chimes, and/or melodies, which may signal differentconditions and/or directions and/or other sensorial or perceptive (e.g.,tactile, gustatory, haptic, pressure-sensing-based or electromagnetic(e.g., neurostimulation) communication devices. The output device 54 maycomprise one or more different display screens presenting various dataand/or interfaces or controls for use by the patient. The output device54 may include graphics, which may be presented by a web-based interfaceand/or by a computer program or application (App.). In some embodiments,the patient interface 50 may include functionality provided by orsimilar to existing voice-based assistants such as Siri by Apple, Alexaby Amazon, Google Assistant, or Bixby by Samsung.

In some embodiments, the output device 54 may present a user interfacethat may present a recommended treatment plan, excluded treatment plan,or the like to the patient. The user interface may include one or moregraphical elements that enable the user to select which treatment planto perform. Responsive to receiving a selection of a graphical element(e.g., “Start” button) associated with a treatment plan via the inputdevice 54, the patient interface 50 may communicate a control signal tothe controller 72 of the treatment apparatus, wherein the control signalcauses the treatment apparatus 70 to begin execution of the selectedtreatment plan. As described below, the control signal may control,based on the selected treatment plan, the treatment apparatus 70 bycausing actuation of the actuator 78 (e.g., cause a motor to driverotation of pedals of the treatment apparatus at a certain speed),causing measurements to be obtained via the sensor 76, or the like. Thepatient interface 50 may communicate, via a local communicationinterface 68, the control signal to the treatment apparatus 70.

As shown in FIG. 1 , the patient interface 50 includes a secondcommunication interface 56, which may also be called a remotecommunication interface configured to communicate with the server 30and/or the clinician interface 20 via a second network 58. In someembodiments, the second network 58 may include a local area network(LAN), such as an Ethernet network. In some embodiments, the secondnetwork 58 may include the Internet, and communications between thepatient interface 50 and the server 30 and/or the clinician interface 20may be secured via encryption, such as, for example, by using a virtualprivate network (VPN). In some embodiments, the second network 58 mayinclude wired and/or wireless network connections such as Wi-Fi,Bluetooth, ZigBee, Near-Field Communications (NFC), cellular datanetwork, etc. In some embodiments, the second network 58 may be the sameas and/or operationally coupled to the first network 34.

The patient interface 50 includes a second processor 60 and a secondmachine-readable storage memory 62 holding second instructions 64 forexecution by the second processor 60 for performing various actions ofpatient interface 50. The second machine-readable storage memory 62 alsoincludes a local data store 66 configured to hold data, such as datapertaining to a treatment plan and/or patient data, such as datarepresenting a patient's performance within a treatment plan. Thepatient interface 50 also includes a local communication interface 68configured to communicate with various devices for use by the patient inthe vicinity of the patient interface 50. The local communicationinterface 68 may include wired and/or wireless communications. In someembodiments, the local communication interface 68 may include a localwireless network such as Wi-Fi, Bluetooth, ZigBee, Near-FieldCommunications (NFC), cellular data network, etc.

The system 10 also includes a treatment apparatus 70 configured to bemanipulated by the patient and/or to manipulate a body part of thepatient for performing activities according to the treatment plan. Insome embodiments, the treatment apparatus 70 may take the form of anexercise and rehabilitation apparatus configured to perform and/or toaid in the performance of a rehabilitation regimen, which may be anorthopedic rehabilitation regimen, and the treatment includesrehabilitation of a body part of the patient, such as a joint or a boneor a muscle group. The treatment apparatus 70 may be any suitablemedical, rehabilitative, therapeutic, etc. apparatus configured to becontrolled distally via another computing device to treat a patientand/or exercise the patient. The treatment apparatus 70 may be anelectromechanical machine including one or more weights, anelectromechanical bicycle, an electromechanical spin-wheel, asmart-mirror, a treadmill, or the like. The body part may include, forexample, a spine, a hand, a foot, a knee, or a shoulder. The body partmay include a part of a joint, a bone, or a muscle group, such as one ormore vertebrae, a tendon, or a ligament. As shown in FIG. 1 , thetreatment apparatus 70 includes a controller 72, which may include oneor more processors, computer memory, and/or other components. Thetreatment apparatus 70 also includes a fourth communication interface 74configured to communicate with the patient interface 50 via the localcommunication interface 68. The treatment apparatus 70 also includes oneor more internal sensors 76 and an actuator 78, such as a motor. Theactuator 78 may be used, for example, for moving the patient's body partand/or for resisting forces by the patient.

The internal sensors 76 may measure one or more operatingcharacteristics of the treatment apparatus 70 such as, for example, aforce, a position, a speed, a velocity, and/or an acceleration. In someembodiments, the internal sensors 76 may include a position sensorconfigured to measure at least one of a linear motion or an angularmotion of a body part of the patient. For example, an internal sensor 76in the form of a position sensor may measure a distance that the patientis able to move a part of the treatment apparatus 70, where suchdistance may correspond to a range of motion that the patient's bodypart is able to achieve. In some embodiments, the internal sensors 76may include a force sensor configured to measure a force applied by thepatient. For example, an internal sensor 76 in the form of a forcesensor may measure a force or weight the patient is able to apply, usinga particular body part, to the treatment apparatus 70.

The system 10 shown in FIG. 1 also includes an ambulation sensor 82,which communicates with the server 30 via the local communicationinterface 68 of the patient interface 50. The ambulation sensor 82 maytrack and store a number of steps taken by the patient. In someembodiments, the ambulation sensor 82 may take the form of a wristband,wristwatch, or smart watch. In some embodiments, the ambulation sensor82 may be integrated within a phone, such as a smartphone.

The system 10 shown in FIG. 1 also includes a goniometer 84, whichcommunicates with the server 30 via the local communication interface 68of the patient interface 50. The goniometer 84 measures an angle of thepatient's body part. For example, the goniometer 84 may measure theangle of flex of a patient's knee or elbow or shoulder.

The system 10 shown in FIG. 1 also includes a pressure sensor 86, whichcommunicates with the server 30 via the local communication interface 68of the patient interface 50. The pressure sensor 86 measures an amountof pressure or weight applied by a body part of the patient. Forexample, pressure sensor 86 may measure an amount of force applied by apatient's foot when pedaling a stationary bike.

The system 10 shown in FIG. 1 also includes a supervisory interface 90which may be similar or identical to the clinician interface 20. In someembodiments, the supervisory interface 90 may have enhancedfunctionality beyond what is provided on the clinician interface 20. Thesupervisory interface 90 may be configured for use by a person havingresponsibility for the treatment plan, such as an orthopedic surgeon.

The system 10 shown in FIG. 1 also includes a reporting interface 92which may be similar or identical to the clinician interface 20. In someembodiments, the reporting interface 92 may have less functionality fromwhat is provided on the clinician interface 20. For example, thereporting interface 92 may not have the ability to modify a treatmentplan. Such a reporting interface 92 may be used, for example, by abiller to determine the use of the system 10 for billing purposes. Inanother example, the reporting interface 92 may not have the ability todisplay patient identifiable information, presenting only pseudonymizeddata and/or anonymized data for certain data fields concerning a datasubject and/or for certain data fields concerning a quasi-identifier ofthe data subject. Such a reporting interface 92 may be used, forexample, by a researcher to determine various effects of a treatmentplan on different patients.

The system 10 includes an assistant interface 94 for an assistant, suchas a doctor, a nurse, a physical therapist, or a technician, to remotelycommunicate with the patient interface 50 and/or the treatment apparatus70. Such remote communications may enable the assistant to provideassistance or guidance to a patient using the system 10. Morespecifically, the assistant interface 94 is configured to communicate atelemedicine signal 96, 97, 98 a, 98 b, 99 a, 99 b with the patientinterface 50 via a network connection such as, for example, via thefirst network 34 and/or the second network 58. The telemedicine signal96, 97, 98 a, 98 b, 99 a, 99 b comprises one of an audio signal 96, anaudiovisual signal 97, an interface control signal 98 a for controllinga function of the patient interface 50, an interface monitor signal 98 bfor monitoring a status of the patient interface 50, an apparatuscontrol signal 99 a for changing an operating parameter of the treatmentapparatus 70, and/or an apparatus monitor signal 99 b for monitoring astatus of the treatment apparatus 70. In some embodiments, each of thecontrol signals 98 a, 99 a may be unidirectional, conveying commandsfrom the assistant interface 94 to the patient interface 50. In someembodiments, in response to successfully receiving a control signal 98a, 99 a and/or to communicate successful and/or unsuccessfulimplementation of the requested control action, an acknowledgementmessage may be sent from the patient interface 50 to the assistantinterface 94. In some embodiments, each of the monitor signals 98 b, 99b may be unidirectional, status-information commands from the patientinterface 50 to the assistant interface 94. In some embodiments, anacknowledgement message may be sent from the assistant interface 94 tothe patient interface 50 in response to successfully receiving one ofthe monitor signals 98 b, 99 b.

In some embodiments, the patient interface 50 may be configured as apass-through for the apparatus control signals 99 a and the apparatusmonitor signals 99 b between the treatment apparatus 70 and one or moreother devices, such as the assistant interface 94 and/or the server 30.For example, the patient interface 50 may be configured to transmit anapparatus control signal 99 a to the treatment apparatus 70 in responseto an apparatus control signal 99 a within the telemedicine signal 96,97, 98 a, 98 b, 99 a, 99 b from the assistant interface 94. In someembodiments, the assistant interface 94 transmits the apparatus controlsignal 99 a (e.g., control instruction that causes an operatingparameter of the treatment apparatus 70 to change) to the treatmentapparatus 70 via any suitable network disclosed herein.

In some embodiments, the assistant interface 94 may be presented on ashared physical device as the clinician interface 20. For example, theclinician interface 20 may include one or more screens that implementthe assistant interface 94. Alternatively or additionally, the clinicianinterface 20 may include additional hardware components, such as a videocamera, a speaker, and/or a microphone, to implement aspects of theassistant interface 94.

In some embodiments, one or more portions of the telemedicine signal 96,97, 98 a, 98 b, 99 a, 99 b may be generated from a prerecorded source(e.g., an audio recording, a video recording, or an animation) forpresentation by the output device 54 of the patient interface 50. Forexample, a tutorial video may be streamed from the server 30 andpresented upon the patient interface 50. Content from the prerecordedsource may be requested by the patient via the patient interface 50.Alternatively, via a control on the assistant interface 94, theassistant may cause content from the prerecorded source to be played onthe patient interface 50.

The assistant interface 94 includes an assistant input device 22 and anassistant display 24, which may be collectively called an assistant userinterface 22, 24. The assistant input device 22 may include one or moreof a telephone, a keyboard, a mouse, a trackpad, or a touch screen, forexample. Alternatively or additionally, the assistant input device 22may include one or more microphones. In some embodiments, the one ormore microphones may take the form of a telephone handset, headset, orwide-area microphone or microphones configured for the assistant tospeak to a patient via the patient interface 50. In some embodiments,assistant input device 22 may be configured to provide voice-basedfunctionalities, with hardware and/or software configured to interpretspoken instructions by the assistant by using the one or moremicrophones. The assistant input device 22 may include functionalityprovided by or similar to existing voice-based assistants such as Siriby Apple, Alexa by Amazon, Google Assistant, or Bixby by Samsung. Theassistant input device 22 may include other hardware and/or softwarecomponents. The assistant input device 22 may include one or moregeneral purpose devices and/or special-purpose devices.

The assistant display 24 may take one or more different forms including,for example, a computer monitor or display screen on a tablet, asmartphone, or a smart watch. The assistant display 24 may include otherhardware and/or software components such as projectors, virtual realitycapabilities, or augmented reality capabilities, etc. The assistantdisplay 24 may incorporate various different visual, audio, or otherpresentation technologies. For example, the assistant display 24 mayinclude a non-visual display, such as an audio signal, which may includespoken language and/or other sounds such as tones, chimes, melodies,and/or compositions, which may signal different conditions and/ordirections. The assistant display 24 may comprise one or more differentdisplay screens presenting various data and/or interfaces or controlsfor use by the assistant. The assistant display 24 may include graphics,which may be presented by a web-based interface and/or by a computerprogram or application (App.).

In some embodiments, the system 10 may provide computer translation oflanguage from the assistant interface 94 to the patient interface 50and/or vice-versa. The computer translation of language may includecomputer translation of spoken language and/or computer translation oftext. Additionally or alternatively, the system 10 may provide voicerecognition and/or spoken pronunciation of text. For example, the system10 may convert spoken words to printed text and/or the system 10 mayaudibly speak language from printed text. The system 10 may beconfigured to recognize spoken words by any or all of the patient, theclinician, and/or the healthcare professional. In some embodiments, thesystem 10 may be configured to recognize and react to spoken requests orcommands by the patient. For example, in response to a verbal command bythe patient (which may be given in any one of several differentlanguages), the system 10 may automatically initiate a telemedicinesession.

In some embodiments, the server 30 may generate aspects of the assistantdisplay 24 for presentation by the assistant interface 94. For example,the server 30 may include a web server configured to generate thedisplay screens for presentation upon the assistant display 24. Forexample, the artificial intelligence engine 11 may generate recommendedtreatment plans and/or excluded treatment plans for patients andgenerate the display screens including those recommended treatment plansand/or external treatment plans for presentation on the assistantdisplay 24 of the assistant interface 94. In some embodiments, theassistant display 24 may be configured to present a virtualized desktophosted by the server 30. In some embodiments, the server 30 may beconfigured to communicate with the assistant interface 94 via the firstnetwork 34. In some embodiments, the first network 34 may include alocal area network (LAN), such as an Ethernet network.

In some embodiments, the first network 34 may include the Internet, andcommunications between the server 30 and the assistant interface 94 maybe secured via privacy enhancing technologies, such as, for example, byusing encryption over a virtual private network (VPN). Alternatively oradditionally, the server 30 may be configured to communicate with theassistant interface 94 via one or more networks independent of the firstnetwork 34 and/or other communication means, such as a direct wired orwireless communication channel. In some embodiments, the patientinterface 50 and the treatment apparatus 70 may each operate from apatient location geographically separate from a location of theassistant interface 94. For example, the patient interface 50 and thetreatment apparatus 70 may be used as part of an in-home rehabilitationsystem, which may be aided remotely by using the assistant interface 94at a centralized location, such as a clinic or a call center.

In some embodiments, the assistant interface 94 may be one of severaldifferent terminals (e.g., computing devices) that may be groupedtogether, for example, in one or more call centers or at one or moreclinicians' offices. In some embodiments, a plurality of assistantinterfaces 94 may be distributed geographically. In some embodiments, aperson may work as an assistant remotely from any conventional officeinfrastructure. Such remote work may be performed, for example, wherethe assistant interface 94 takes the form of a computer and/ortelephone. This remote work functionality may allow for work-from-homearrangements that may include part time and/or flexible work hours foran assistant.

FIGS. 2-3 show an embodiment of a treatment apparatus 70. Morespecifically, FIG. 2 shows a treatment apparatus 70 in the form of astationary cycling machine 100, which may be called a stationary bike,for short. The stationary cycling machine 100 includes a set of pedals102 each attached to a pedal arm 104 for rotation about an axle 106. Insome embodiments, and as shown in FIG. 2 , the pedals 102 are movable onthe pedal arms 104 in order to adjust a range of motion used by thepatient in pedaling. For example, the pedals being located inwardlytoward the axle 106 corresponds to a smaller range of motion than whenthe pedals are located outwardly away from the axle 106. A pressuresensor 86 is attached to or embedded within one of the pedals 102 formeasuring an amount of force applied by the patient on the pedal 102.The pressure sensor 86 may communicate wirelessly to the treatmentapparatus 70 and/or to the patient interface 50.

FIG. 4 shows a person (a patient) using the treatment apparatus of FIG.2 , and showing sensors and various data parameters connected to apatient interface 50. The example patient interface 50 is a tabletcomputer or smartphone, or a phablet, such as an iPad, an iPhone, anAndroid device, or a Surface tablet, which is held manually by thepatient. In some other embodiments, the patient interface 50 may beembedded within or attached to the treatment apparatus 70. FIG. 4 showsthe patient wearing the ambulation sensor 82 on his wrist, with a noteshowing “STEPS TODAY 1355”, indicating that the ambulation sensor 82 hasrecorded and transmitted that step count to the patient interface 50.FIG. 4 also shows the patient wearing the goniometer 84 on his rightknee, with a note showing “KNEE ANGLE 72° ”, indicating that thegoniometer 84 is measuring and transmitting that knee angle to thepatient interface 50. FIG. 4 also shows a right side of one of thepedals 102 with a pressure sensor 86 showing “FORCE 12.5 lbs.,”indicating that the right pedal pressure sensor 86 is measuring andtransmitting that force measurement to the patient interface 50. FIG. 4also shows a left side of one of the pedals 102 with a pressure sensor86 showing “FORCE 27 lbs.”, indicating that the left pedal pressuresensor 86 is measuring and transmitting that force measurement to thepatient interface 50. FIG. 4 also shows other patient data, such as anindicator of “SESSION TIME 0:04:13”, indicating that the patient hasbeen using the treatment apparatus 70 for 4 minutes and 13 seconds. Thissession time may be determined by the patient interface 50 based oninformation received from the treatment apparatus 70. FIG. 4 also showsan indicator showing “PAIN LEVEL 3”. Such a pain level may be obtainedfrom the patent in response to a solicitation, such as a question,presented upon the patient interface 50.

FIG. 5 is an example embodiment of an overview display 120 of theassistant interface 94. Specifically, the overview display 120 presentsseveral different controls and interfaces for the assistant to remotelyassist a patient with using the patient interface 50 and/or thetreatment apparatus 70. This remote assistance functionality may also becalled telemedicine or telehealth.

Specifically, the overview display 120 includes a patient profiledisplay 130 presenting biographical information regarding a patientusing the treatment apparatus 70. The patient profile display 130 maytake the form of a portion or region of the overview display 120, asshown in FIG. 5 , although the patient profile display 130 may takeother forms, such as a separate screen or a popup window. In someembodiments, the patient profile display 130 may include a limitedsubset of the patient's biographical information. More specifically, thedata presented upon the patient profile display 130 may depend upon theassistant's need for that information. For example, a healthcareprofessional that is assisting the patient with a medical issue may beprovided with medical history information regarding the patient, whereasa technician troubleshooting an issue with the treatment apparatus 70may be provided with a much more limited set of information regardingthe patient. The technician, for example, may be given only thepatient's name. The patient profile display 130 may includepseudonymized data and/or anonymized data or use any privacy enhancingtechnology to prevent confidential patient data from being communicatedin a way that could violate patient confidentiality requirements. Suchprivacy enhancing technologies may enable compliance with laws,regulations, or other rules of governance such as, but not limited to,the Health Insurance Portability and Accountability Act (HIPAA), or theGeneral Data Protection Regulation (GDPR), wherein the patient may bedeemed a “data subject”.

In some embodiments, the patient profile display 130 may presentinformation regarding the treatment plan for the patient to follow inusing the treatment apparatus 70. Such treatment plan information may belimited to an assistant who is a healthcare professional, such as adoctor or physical therapist. For example, a healthcare professionalassisting the patient with an issue regarding the treatment regimen maybe provided with treatment plan information, whereas a techniciantroubleshooting an issue with the treatment apparatus 70 may not beprovided with any information regarding the patient's treatment plan.

In some embodiments, one or more recommended treatment plans and/orexcluded treatment plans may be presented in the patient profile display130 to the assistant. The one or more recommended treatment plans and/orexcluded treatment plans may be generated by the artificial intelligenceengine 11 of the server 30 and received from the server 30 in real-timeduring, inter alia, a telemedicine or telehealth session. An example ofpresenting the one or more recommended treatment plans and/or excludedtreatment plans is described below with reference to FIG. 7 .

The example overview display 120 shown in FIG. 5 also includes a patientstatus display 134 presenting status information regarding a patientusing the treatment apparatus. The patient status display 134 may takethe form of a portion or region of the overview display 120, as shown inFIG. 5 , although the patient status display 134 may take other forms,such as a separate screen or a popup window. The patient status display134 includes sensor data 136 from one or more of the external sensors82, 84, 86, and/or from one or more internal sensors 76 of the treatmentapparatus 70. In some embodiments, the patient status display 134 mayinclude sensor data from one or more sensors of one or more wearabledevices worn by the patient while using the treatment device 70. The oneor more wearable devices may include a watch, a bracelet, a necklace, achest strap, and the like. The one or more wearable devices may beconfigured to monitor a heartrate, a temperature, a blood pressure, oneor more vital signs, and the like of the patient while the patient isusing the treatment device 70. In some embodiments, the patient statusdisplay 134 may present other data 138 regarding the patient, such aslast reported pain level, or progress within a treatment plan.

User access controls may be used to limit access, including what data isavailable to be viewed and/or modified, on any or all of the userinterfaces 20, 50, 90, 92, 94 of the system 10. In some embodiments,user access controls may be employed to control what information isavailable to any given person using the system 10. For example, datapresented on the assistant interface 94 may be controlled by user accesscontrols, with permissions set depending on the assistant/user's needfor and/or qualifications to view that information.

The example overview display 120 shown in FIG. 5 also includes a helpdata display 140 presenting information for the assistant to use inassisting the patient. The help data display 140 may take the form of aportion or region of the overview display 120, as shown in FIG. 5 . Thehelp data display 140 may take other forms, such as a separate screen ora popup window. The help data display 140 may include, for example,presenting answers to frequently asked questions regarding use of thepatient interface 50 and/or the treatment apparatus 70. The help datadisplay 140 may also include research data or best practices. In someembodiments, the help data display 140 may present scripts for answersor explanations in response to patient questions. In some embodiments,the help data display 140 may present flow charts or walk-throughs forthe assistant to use in determining a root cause and/or solution to apatient's problem. In some embodiments, the assistant interface 94 maypresent two or more help data displays 140, which may be the same ordifferent, for simultaneous presentation of help data for use by theassistant. for example, a first help data display may be used to presenta troubleshooting flowchart to determine the source of a patient'sproblem, and a second help data display may present script informationfor the assistant to read to the patient, such information to preferablyinclude directions for the patient to perform some action, which mayhelp to narrow down or solve the problem. In some embodiments, basedupon inputs to the troubleshooting flowchart in the first help datadisplay, the second help data display may automatically populate withscript information.

The example overview display 120 shown in FIG. 5 also includes a patientinterface control 150 presenting information regarding the patientinterface 50, and/or to modify one or more settings of the patientinterface 50. The patient interface control 150 may take the form of aportion or region of the overview display 120, as shown in FIG. 5 . Thepatient interface control 150 may take other forms, such as a separatescreen or a popup window. The patient interface control 150 may presentinformation communicated to the assistant interface 94 via one or moreof the interface monitor signals 98 b. As shown in FIG. 5 , the patientinterface control 150 includes a display feed 152 of the displaypresented by the patient interface 50. In some embodiments, the displayfeed 152 may include a live copy of the display screen currently beingpresented to the patient by the patient interface 50. In other words,the display feed 152 may present an image of what is presented on adisplay screen of the patient interface 50. In some embodiments, thedisplay feed 152 may include abbreviated information regarding thedisplay screen currently being presented by the patient interface 50,such as a screen name or a screen number. The patient interface control150 may include a patient interface setting control 154 for theassistant to adjust or to control one or more settings or aspects of thepatient interface 50. In some embodiments, the patient interface settingcontrol 154 may cause the assistant interface 94 to generate and/or totransmit an interface control signal 98 for controlling a function or asetting of the patient interface 50.

In some embodiments, the patient interface setting control 154 mayinclude collaborative browsing or co-browsing capability for theassistant to remotely view and/or control the patient interface 50. Forexample, the patient interface setting control 154 may enable theassistant to remotely enter text to one or more text entry fields on thepatient interface 50 and/or to remotely control a cursor on the patientinterface 50 using a mouse or touchscreen of the assistant interface 94.

In some embodiments, using the patient interface 50, the patientinterface setting control 154 may allow the assistant to change asetting that cannot be changed by the patient. For example, the patientinterface 50 may be precluded from accessing a language setting toprevent a patient from inadvertently switching, on the patient interface50, the language used for the displays, whereas the patient interfacesetting control 154 may enable the assistant to change the languagesetting of the patient interface 50. In another example, the patientinterface 50 may not be able to change a font size setting to a smallersize in order to prevent a patient from inadvertently switching the fontsize used for the displays on the patient interface 50 such that thedisplay would become illegible to the patient, whereas the patientinterface setting control 154 may provide for the assistant to changethe font size setting of the patient interface 50.

The example overview display 120 shown in FIG. 5 also includes aninterface communications display 156 showing the status ofcommunications between the patient interface 50 and one or more otherdevices 70, 82, 84, such as the treatment apparatus 70, the ambulationsensor 82, and/or the goniometer 84. The interface communicationsdisplay 156 may take the form of a portion or region of the overviewdisplay 120, as shown in FIG. 5 . The interface communications display156 may take other forms, such as a separate screen or a popup window.The interface communications display 156 may include controls for theassistant to remotely modify communications with one or more of theother devices 70, 82, 84. For example, the assistant may remotelycommand the patient interface 50 to reset communications with one of theother devices 70, 82, 84, or to establish communications with a new oneof the other devices 70, 82, 84. This functionality may be used, forexample, where the patient has a problem with one of the other devices70, 82, 84, or where the patient receives a new or a replacement one ofthe other devices 70, 82, 84.

The example overview display 120 shown in FIG. 5 also includes anapparatus control 160 for the assistant to view and/or to controlinformation regarding the treatment apparatus 70. The apparatus control160 may take the form of a portion or region of the overview display120, as shown in FIG. 5 . The apparatus control 160 may take otherforms, such as a separate screen or a popup window. The apparatuscontrol 160 may include an apparatus status display 162 with informationregarding the current status of the apparatus. The apparatus statusdisplay 162 may present information communicated to the assistantinterface 94 via one or more of the apparatus monitor signals 99 b. Theapparatus status display 162 may indicate whether the treatmentapparatus 70 is currently communicating with the patient interface 50.The apparatus status display 162 may present other current and/orhistorical information regarding the status of the treatment apparatus70.

The apparatus control 160 may include an apparatus setting control 164for the assistant to adjust or control one or more aspects of thetreatment apparatus 70. The apparatus setting control 164 may cause theassistant interface 94 to generate and/or to transmit an apparatuscontrol signal 99 a for changing an operating parameter of the treatmentapparatus 70, (e.g., a pedal radius setting, a resistance setting, atarget RPM, other suitable characteristics of the treatment device 70,or a combination thereof).

The apparatus setting control 164 may include a mode button 166 and aposition control 168, which may be used in conjunction for the assistantto place an actuator 78 of the treatment apparatus 70 in a manual mode,after which a setting, such as a position or a speed of the actuator 78,can be changed using the position control 168. The mode button 166 mayprovide for a setting, such as a position, to be toggled betweenautomatic and manual modes. In some embodiments, one or more settingsmay be adjustable at any time, and without having an associatedauto/manual mode. In some embodiments, the assistant may change anoperating parameter of the treatment apparatus 70, such as a pedalradius setting, while the patient is actively using the treatmentapparatus 70. Such “on the fly” adjustment may or may not be availableto the patient using the patient interface 50. In some embodiments, theapparatus setting control 164 may allow the assistant to change asetting that cannot be changed by the patient using the patientinterface 50. For example, the patient interface 50 may be precludedfrom changing a preconfigured setting, such as a height or a tiltsetting of the treatment apparatus 70, whereas the apparatus settingcontrol 164 may provide for the assistant to change the height or tiltsetting of the treatment apparatus 70.

The example overview display 120 shown in FIG. 5 also includes a patientcommunications control 170 for controlling an audio or an audiovisualcommunications session with the patient interface 50. The communicationssession with the patient interface 50 may comprise a live feed from theassistant interface 94 for presentation by the output device of thepatient interface 50. The live feed may take the form of an audio feedand/or a video feed. In some embodiments, the patient interface 50 maybe configured to provide two-way audio or audiovisual communicationswith a person using the assistant interface 94. Specifically, thecommunications session with the patient interface 50 may includebidirectional (two-way) video or audiovisual feeds, with each of thepatient interface 50 and the assistant interface 94 presenting video ofthe other one. In some embodiments, the patient interface 50 may presentvideo from the assistant interface 94, while the assistant interface 94presents only audio or the assistant interface 94 presents no live audioor visual signal from the patient interface 50. In some embodiments, theassistant interface 94 may present video from the patient interface 50,while the patient interface 50 presents only audio or the patientinterface 50 presents no live audio or visual signal from the assistantinterface 94.

In some embodiments, the audio or an audiovisual communications sessionwith the patient interface 50 may take place, at least in part, whilethe patient is performing the rehabilitation regimen upon the body part.The patient communications control 170 may take the form of a portion orregion of the overview display 120, as shown in FIG. 5 . The patientcommunications control 170 may take other forms, such as a separatescreen or a popup window. The audio and/or audiovisual communicationsmay be processed and/or directed by the assistant interface 94 and/or byanother device or devices, such as a telephone system, or avideoconferencing system used by the assistant while the assistant usesthe assistant interface 94. Alternatively or additionally, the audioand/or audiovisual communications may include communications with athird party. For example, the system 10 may enable the assistant toinitiate a 3-way conversation regarding use of a particular piece ofhardware or software, with the patient and a subject matter expert, suchas a medical professional or a specialist. The example patientcommunications control 170 shown in FIG. 5 includes call controls 172for the assistant to use in managing various aspects of the audio oraudiovisual communications with the patient. The call controls 172include a disconnect button 174 for the assistant to end the audio oraudiovisual communications session. The call controls 172 also include amute button 176 to temporarily silence an audio or audiovisual signalfrom the assistant interface 94. In some embodiments, the call controls172 may include other features, such as a hold button (not shown). Thecall controls 172 also include one or more record/playback controls 178,such as record, play, and pause buttons to control, with the patientinterface 50, recording and/or playback of audio and/or video from theteleconference session (e.g., which may be referred to herein as thevirtual conference room). The call controls 172 also include a videofeed display 180 for presenting still and/or video images from thepatient interface 50, and a self-video display 182 showing the currentimage of the assistant using the assistant interface. The self-videodisplay 182 may be presented as a picture-in-picture format, within asection of the video feed display 180, as shown in FIG. 5 .Alternatively or additionally, the self-video display 182 may bepresented separately and/or independently from the video feed display180.

The example overview display 120 shown in FIG. 5 also includes athird-party communications control 190 for use in conducting audioand/or audiovisual communications with a third party. The third-partycommunications control 190 may take the form of a portion or region ofthe overview display 120, as shown in FIG. 5 . The third-partycommunications control 190 may take other forms, such as a display on aseparate screen or a popup window. The third-party communicationscontrol 190 may include one or more controls, such as a contact listand/or buttons or controls to contact a third party regarding use of aparticular piece of hardware or software, e.g., a subject matter expert,such as a medical professional or a specialist. The third-partycommunications control 190 may include conference calling capability forthe third party to simultaneously communicate with both the assistantvia the assistant interface 94, and with the patient via the patientinterface 50. For example, the system 10 may provide for the assistantto initiate a 3-way conversation with the patient and the third party.

FIG. 6 shows an example block diagram of training a machine learningmodel 13 to output, based on data 600 pertaining to the patient, atreatment plan 602 for the patient according to the present disclosure.Data pertaining to other patients may be received by the server 30. Theother patients may have used various treatment apparatuses to performtreatment plans. The data may include characteristics of the otherpatients, the details of the treatment plans performed by the otherpatients, and/or the results of performing the treatment plans (e.g., apercent of recovery of a portion of the patients' bodies, an amount ofrecovery of a portion of the patients' bodies, an amount of increase ordecrease in muscle strength of a portion of patients' bodies, an amountof increase or decrease in range of motion of a portion of patients'bodies, etc.).

As depicted, the data has been assigned to different cohorts. Cohort Aincludes data for patients having similar first characteristics, firsttreatment plans, and first results. Cohort B includes data for patientshaving similar second characteristics, second treatment plans, andsecond results. For example, cohort A may include first characteristicsof patients in their twenties without any medical conditions whounderwent surgery for a broken limb; their treatment plans may include acertain treatment protocol (e.g., use the treatment apparatus 70 for 30minutes 5 times a week for 3 weeks, wherein values for the properties,configurations, and/or settings of the treatment apparatus 70 are set toX (where X is a numerical value) for the first two weeks and to Y (whereY is a numerical value) for the last week).

Cohort A and cohort B may be included in a training dataset used totrain the machine learning model 13. The machine learning model 13 maybe trained to match a pattern between characteristics for each cohortand output the treatment plan that provides the result. Accordingly,when the data 600 for a new patient is input into the trained machinelearning model 13, the trained machine learning model 13 may match thecharacteristics included in the data 600 with characteristics in eithercohort A or cohort B and output the appropriate treatment plan 602. Insome embodiments, the machine learning model 13 may be trained to outputone or more excluded treatment plans that should not be performed by thenew patient.

FIG. 7 shows an embodiment of an overview display 120 of the assistantinterface 94 presenting recommended treatment plans and excludedtreatment plans in real-time during a telemedicine session according tothe present disclosure. As depicted, the overview display 120 onlyincludes sections for the patient profile 130 and the video feed display180, including the self-video display 182. Any suitable configuration ofcontrols and interfaces of the overview display 120 described withreference to FIG. 5 may be presented in addition to or instead of thepatient profile 130, the video feed display 180, and the self-videodisplay 182.

The healthcare professional using the assistant interface 94 (e.g.,computing device) during the telemedicine session may be presented inthe self-video 182 in a portion of the overview display 120 (e.g., userinterface presented on a display screen 24 of the assistant interface94) that also presents a video from the patient in the video feeddisplay 180. Further, the video feed display 180 may also include agraphical user interface (GUI) object 700 (e.g., a button) that enablesthe healthcare professional to share on the patient interface 50, inreal-time or near real-time during the telemedicine session, therecommended treatment plans and/or the excluded treatment plans with thepatient. The healthcare professional may select the GUI object 700 toshare the recommended treatment plans and/or the excluded treatmentplans. As depicted, another portion of the overview display 120 includesthe patient profile display 130.

In FIG. 7 , the patient profile display 130 is presenting two examplerecommended treatment plans 708 and one example excluded treatment plan710. As described herein, the treatment plans may be recommended basedon the one or more probabilities and the respective measure of benefitthe one or more exercises provide the user. The trained machine learningmodels 13 may (i) use treatment data pertaining to a user to determine arespective measure of benefit which one or more exercise regimensprovide the user, (ii) determine one or more probabilities of the userassociated with complying with the one or more exercise regimens, and(iii) generate, using the one or more probabilities and the respectivemeasure of benefit the one or more exercises provide to the user, thetreatment plan. In some embodiments, the one or more trained machinelearning models 13 may generate treatment plans including exercisesassociated with a certain threshold (e.g., any suitable percentagemetric, value, percentage, number, indicator, probability, etc., whichmay be configurable) associated with the user complying with the one ormore exercise regimens to enable achieving a higher user compliance withthe treatment plan. In some embodiments, the one or more trained machinelearning models 13 may generate treatment plans including exercisesassociated with a certain threshold (e.g., any suitable percentagemetric, value, percentage, number, indicator, probability, etc., whichmay be configurable) associated with one or more measures of benefit theexercises provide to the user to enable achieving the benefits (e.g.,strength, flexibility, range of motion, etc.) at a faster rate, at agreater proportion, etc. In some embodiments, when both the measures ofbenefit and the probability of compliance are considered by the trainedmachine learning models 13, each of the measures of benefit and theprobability of compliance may be associated with a different weight,such different weight causing one to be more influential than the other.Such techniques may enable configuring which parameter (e.g.,probability of compliance or measures of benefit) is more desirable toconsider more heavily during generation of the treatment plan.

For example, as depicted, the patient profile display 130 presents “Thefollowing treatment plans are recommended for the patient based on oneor more probabilities of the user complying with one or more exerciseregimens and the respective measure of benefit the one or more exercisesprovide the user.” Then, the patient profile display 130 presents afirst recommended treatment plan.

As depicted, treatment plan “1” indicates “Patient X should usetreatment apparatus for 30 minutes a day for 4 days to achieve anincreased range of motion of Y %. The exercises include a first exerciseof pedaling the treatment apparatus for 30 minutes at a range of motionof Z % at 5 miles per hour, a second exercise of pedaling the treatmentapparatus for 30 minutes at a range of motion of Y % at 10 miles perhour, etc. The first and second exercise satisfy a threshold complianceprobability and/or a threshold measure of benefit which the exerciseregimens provide to the user.” Accordingly, the treatment plan generatedincludes a first and second exercise, etc. that increase the range ofmotion of Y %. Further, in some embodiments, the exercises are indicatedas satisfying a threshold compliance probability and/or a thresholdmeasure of benefit which the exercise regimens provide to the user. Eachof the exercises may specify any suitable parameter of the exerciseand/or treatment apparatus 70 (e.g., duration of exercise, speed ofmotor of the treatment apparatus 70, range of motion setting of pedals,etc.). This specific example and all such examples elsewhere herein arenot intended to limit in any way the generated treatment plan fromrecommending any suitable number and/or type of exercise.

Recommended treatment plan “2” may specify, based on a desired benefit,an indication of a probability of compliance, or some combinationthereof, and different exercises for the user to perform.

As depicted, the patient profile display 130 may also present theexcluded treatment plans 710. These types of treatment plans are shownto the assistant using the assistant interface 94 to alert the assistantnot to recommend certain portions of a treatment plan to the patient.For example, the excluded treatment plan could specify the following:“Patient X should not use treatment apparatus for longer than 30 minutesa day due to a heart condition.” Specifically, the excluded treatmentplan points out a limitation of a treatment protocol where, due to aheart condition, Patient X should not exercise for more than 30 minutesa day. The excluded treatment plans may be based on treatment data(e.g., characteristics of the user, characteristics of the treatmentapparatus 70, or the like).

The assistant may select the treatment plan for the patient on theoverview display 120. For example, the assistant may use an inputperipheral (e.g., mouse, touchscreen, microphone, keyboard, etc.) toselect from the treatment plans 708 for the patient.

In any event, the assistant may select the treatment plan for thepatient to follow to achieve a desired result. The selected treatmentplan may be transmitted to the patient interface 50 for presentation.The patient may view the selected treatment plan on the patientinterface 50. In some embodiments, the assistant and the patient maydiscuss during the telemedicine session the details (e.g., treatmentprotocol using treatment apparatus 70, diet regimen, medication regimen,etc.) in real-time or in near real-time. In some embodiments, asdiscussed further with reference to method 1000 of FIG. 10 below, theserver 30 may control, based on the selected treatment plan and duringthe telemedicine session, the treatment apparatus 70 as the user usesthe treatment apparatus 70.

FIG. 8 shows an example embodiment of a method 800 for optimizing atreatment plan for a user to increase a probability of the usercomplying with the treatment plan according to the present disclosure.The method 800 is performed by processing logic that may includehardware (circuitry, dedicated logic, etc.), software (such as is run ona general-purpose computer system or a dedicated machine), or acombination of both. The method 800 and/or each of its individualfunctions, routines, other methods, scripts, subroutines, or operationsmay be performed by one or more processors of a computing device (e.g.,any component of FIG. 1 , such as server 30 executing the artificialintelligence engine 11). In certain implementations, the method 800 maybe performed by a single processing thread. Alternatively, the method800 may be performed by two or more processing threads, each threadimplementing one or more individual functions or routines; or othermethods, scripts, subroutines, or operations of the methods.

For simplicity of explanation, the method 800 is depicted and describedas a series of operations. However, operations in accordance with thisdisclosure can occur in various orders and/or concurrently, and/or withother operations not presented and described herein. For example, theoperations depicted in the method 800 may occur in combination with anyother operation of any other method disclosed herein. Furthermore, notall illustrated operations may be required to implement the method 800in accordance with the disclosed subject matter. In addition, thoseskilled in the art will understand and appreciate that the method 800could alternatively be represented as a series of interrelated statesvia a state diagram, a directed graph, a deterministic finite stateautomaton, a non-deterministic finite state automaton, a Markov diagram,or event diagrams.

At 802, the processing device may receive treatment data pertaining to auser (e.g., patient, volunteer, trainee, assistant, healthcareprofessional, instructor, etc.). The treatment data may include one ormore characteristics (e.g., vital-sign or other measurements;performance; demographic; psychographic; geographic; diagnostic;measurement- or test-based; medically historic; etiologic;cohort-associative; differentially diagnostic; surgical, physicallytherapeutic, pharmacologic and other treatment(s) recommended; arterialblood gas and/or oxygenation levels or percentages; psychographics;etc.) of the user. The treatment data may include one or morecharacteristics of the treatment apparatus 70. In some embodiments, theone or more characteristics of the treatment apparatus 70 may include amake (e.g., identity of entity that designed, manufactured, etc. thetreatment apparatus 70) of the treatment apparatus 70, a model (e.g.,model number or other identifier of the model) of the treatmentapparatus 70, a year (e.g., year of manufacturing) of the treatmentapparatus 70, operational parameters (e.g., motor temperature duringoperation; status of each sensor included in or associated with thetreatment apparatus 70; the patient, or the environment; vibrationmeasurements of the treatment apparatus 70 in operation; measurements ofstatic and/or dynamic forces exerted on the treatment apparatus 70;etc.) of the treatment apparatus 70, settings (e.g., range of motionsetting; speed setting; required pedal force setting; etc.) of thetreatment apparatus 70, and the like. In some embodiments, thecharacteristics of the user and/or the characteristics of the treatmentapparatus 70 may be tracked over time to obtain historical datapertaining to the characteristics of the user and/or the treatmentapparatus 70. The foregoing embodiments shall also be deemed to includethe use of any optional internal components or of any externalcomponents attachable to, but separate from the treatment apparatusitself “Attachable” as used herein shall be physically, electronically,mechanically, virtually or in an augmented reality manner.

In some embodiments, when generating a treatment plan, thecharacteristics of the user and/or treatment apparatus 70 may be used.For example, certain exercises may be selected or excluded based on thecharacteristics of the user and/or treatment apparatus 70. For example,if the user has a heart condition, high intensity exercises may beexcluded in a treatment plan. In another example, a characteristic ofthe treatment apparatus 70 may indicate the motor shudders, stalls orotherwise runs improperly at a certain number of revolutions per minute.In order to extend the lifetime of the treatment apparatus 70, thetreatment plan may exclude exercises that include operating the motor atthat certain revolutions per minute or at a prescribed manufacturingtolerance within those certain revolutions per minute.

At 804, the processing device may determine, via one or more trainedmachine learning models 13, a respective measure of benefit with whichone or more exercises provide the user. In some embodiments, based onthe treatment data, the processing device may execute the one or moretrained machine learning models 13 to determine the respective measuresof benefit. For example, the treatment data may include thecharacteristics of the user (e.g., heartrate, vital-sign, medicalcondition, injury, surgery, etc.), and the one or more trained machinelearning models may receive the treatment data and output the respectivemeasure of benefit with which one or more exercises provide the user.For example, if the user has a heart condition, a high intensityexercise may provide a negative benefit to the user, and thus, thetrained machine learning model may output a negative measure of benefitfor the high intensity exercise for the user. In another example, anexercise including pedaling at a certain range of motion may have apositive benefit for a user recovering from a certain surgery, and thus,the trained machine learning model may output a positive measure ofbenefit for the exercise regimen for the user.

At 806, the processing device may determine, via the one or more trainedmachine learning models 13, one or more probabilities associated withthe user complying with the one or more exercise regimens. In someembodiments, the relationship between the one or more probabilitiesassociated with the user complying with the one or more exerciseregimens may be one to one, one to many, many to one, or many to many.The one or more probabilities of compliance may refer to a metric (e.g.,value, percentage, number, indicator, probability, etc.) associated witha probability the user will comply with an exercise regimen. In someembodiments, the processing device may execute the one or more trainedmachine learning models 13 to determine the one or more probabilitiesbased on (i) historical data pertaining to the user, another user, orboth, (ii) received feedback from the user, another user, or both, (iii)received feedback from a treatment apparatus used by the user, or (iv)some combination thereof.

For example, historical data pertaining to the user may indicate ahistory of the user previously performing one or more of the exercises.In some instances, at a first time, the user may perform a firstexercise to completion. At a second time, the user may terminate asecond exercise prior to completion. Feedback data from the user and/orthe treatment apparatus 70 may be obtained before, during, and aftereach exercise performed by the user. The trained machine learning modelmay use any combination of data (e.g., (i) historical data pertaining tothe user, another user, or both, (ii) received feedback from the user,another user, or both, (iii) received feedback from a treatmentapparatus used by the user) described above to learn a user complianceprofile for each of the one or more exercises. The term “user complianceprofile” may refer to a collection of histories of the user complyingwith the one or more exercise regimens. In some embodiments, the trainedmachine learning model may use the user compliance profile, among otherdata (e.g., characteristics of the treatment apparatus 70), to determinethe one or more probabilities of the user complying with the one or moreexercise regimens.

At 808, the processing device may transmit a treatment plan to acomputing device. The computing device may be any suitable interfacedescribed herein. For example, the treatment plan may be transmitted tothe assistant interface 94 for presentation to a healthcareprofessional, and/or to the patient interface 50 for presentation to thepatient. The treatment plan may be generated based on the one or moreprobabilities and the respective measure of benefit the one or moreexercises may provide to the user. In some embodiments, as describedfurther below with reference to the method 1000 of FIG. 10 , while theuser uses the treatment apparatus 70, the processing device may control,based on the treatment plan, the treatment apparatus 70.

In some embodiments, the processing device may generate, using at leasta subset of the one or more exercises, the treatment plan for the userto perform, wherein such performance uses the treatment apparatus 70.The processing device may execute the one or more trained machinelearning models 13 to generate the treatment plan based on therespective measure of the benefit the one or more exercises provide tothe user, the one or more probabilities associated with the usercomplying with each of the one or more exercise regimens, or somecombination thereof. For example, the one or more trained machinelearning models 13 may receive the respective measure of the benefit theone or more exercises provide to the user, the one or more probabilitiesof the user associated with complying with each of the one or moreexercise regimens, or some combination thereof as input and output thetreatment plan.

In some embodiments, during generation of the treatment plan, theprocessing device may more heavily or less heavily weight theprobability of the user complying than the respective measure of benefitthe one or more exercise regimens provide to the user. During generationof the treatment plan, such a technique may enable one of the factors(e.g., the probability of the user complying or the respective measureof benefit the one or more exercise regimens provide to the user) tobecome more important than the other factor. For example, if desirableto select exercises that the user is more likely to comply with in atreatment plan, then the one or more probabilities of the userassociated with complying with each of the one or more exercise regimensmay receive a higher weight than one or more measures of exercisebenefit factors. In another example, if desirable to obtain certainbenefits provided by exercises, then the measure of benefit an exerciseregimen provides to a user may receive a higher weight than the usercompliance probability factor. The weight may be any suitable value,number, modifier, percentage, probability, etc.

In some embodiments, the processing device may generate the treatmentplan using a non-parametric model, a parametric model, or a combinationof both a non-parametric model and a parametric model. In statistics, aparametric model or finite-dimensional model refers to probabilitydistributions that have a finite number of parameters. Non-parametricmodels include model structures not specified a priori but insteaddetermined from data. In some embodiments, the processing device maygenerate the treatment plan using a probability density function, aBayesian prediction model, a Markovian prediction model, or any othersuitable mathematically-based prediction model. A Bayesian predictionmodel is used in statistical inference where Bayes' theorem is used toupdate the probability for a hypothesis as more evidence or informationbecomes available. Bayes' theorem may describe the probability of anevent, based on prior knowledge of conditions that might be related tothe event. For example, as additional data (e.g., user compliance datafor certain exercises, characteristics of users, characteristics oftreatment apparatuses, and the like) are obtained, the probabilities ofcompliance for users for performing exercise regimens may becontinuously updated. The trained machine learning models 13 may use theBayesian prediction model and, in preferred embodiments, continuously,constantly or frequently be re-trained with additional data obtained bythe artificial intelligence engine 11 to update the probabilities ofcompliance, and/or the respective measure of benefit one or moreexercises may provide to a user.

In some embodiments, the processing device may generate the treatmentplan based on a set of factors. In some embodiments, the set of factorsmay include an amount, quality or other quality of sleep associated withthe user, information pertaining to a diet of the user, informationpertaining to an eating schedule of the user, information pertaining toan age of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user, anindication of an energy level of the user, or some combination thereof.For example, the set of factors may be included in the training dataused to train and/or re-train the one or more machine learning models13. For example, the set of factors may be labeled as corresponding totreatment data indicative of certain measures of benefit one or moreexercises provide to the user, probabilities of the user complying withthe one or more exercise regimens, or both.

FIG. 9 shows an example embodiment of a method 900 for generating atreatment plan based on a desired benefit, a desired pain level, anindication of a probability associated with complying with theparticular exercise regimen, or some combination thereof, according tosome embodiments. Method 900 includes operations performed by processorsof a computing device (e.g., any component of FIG. 1 , such as server 30executing the artificial intelligence engine 11). In some embodiments,one or more operations of the method 900 are implemented in computerinstructions stored on a memory device and executed by a processingdevice. The method 900 may be performed in the same or a similar manneras described above in regard to method 800. The operations of the method900 may be performed in some combination with any of the operations ofany of the methods described herein.

At 902, the processing device may receive user input pertaining to adesired benefit, a desired pain level, an indication of a probabilityassociated with complying with a particular exercise regimen, or somecombination thereof. The user input may be received from the patientinterface 50. That is, in some embodiments, the patient interface 50 maypresent a display including various graphical elements that enable theuser to enter a desired benefit of performing an exercise, a desiredpain level (e.g., on a scale ranging from 1-10, 1 being the lowest painlevel and 10 being the highest pain level), an indication of aprobability associated with complying with the particular exerciseregimen, or some combination thereof. For example, the user may indicatehe or she would not comply with certain exercises (e.g., one-armpush-ups) included in an exercise regimen due to a lack of ability toperform the exercise and/or a lack of desire to perform the exercise.The patient interface 50 may transmit the user input to the processingdevice (e.g., of the server 30, assistant interface 94, or any suitableinterface described herein).

At 904, the processing device may generate, using at least a subset ofthe one or more exercises, the treatment plan for the user to performwherein the performance uses the treatment apparatus 70. The processingdevice may generate the treatment plan based on the user input includingthe desired benefit, the desired pain level, the indication of theprobability associated with complying with the particular exerciseregimen, or some combination thereof. For example, if the user selecteda desired benefit of improved range of motion of flexion and extensionof their knee, then the one or more trained machine learning models 13may identify, based on treatment data pertaining to the user, exercisesthat provide the desired benefit. Those identified exercises may befurther filtered based on the probabilities of user compliance with theexercise regimens. Accordingly, the one or more machine learning models13 may be interconnected, such that the output of one or more trainedmachine learning models that perform function(s) (e.g., determinemeasures of benefit exercises provide to user) may be provided as inputto one or more other trained machine learning models that perform otherfunctions(s) (e.g., determine probabilities of the user complying withthe one or more exercise regimens, generate the treatment plan based onthe measures of benefit and/or the probabilities of the user complying,etc.).

FIG. 10 shows an example embodiment of a method 1000 for controlling,based on a treatment plan, a treatment apparatus 70 while a user usesthe treatment apparatus 70, according to some embodiments. Method 1000includes operations performed by processors of a computing device (e.g.,any component of FIG. 1 , such as server 30 executing the artificialintelligence engine 11). In some embodiments, one or more operations ofthe method 1000 are implemented in computer instructions stored on amemory device and executed by a processing device. The method 1000 maybe performed in the same or a similar manner as described above inregard to method 800. The operations of the method 1000 may be performedin some combination with any of the operations of any of the methodsdescribed herein.

At 1002, the processing device may transmit, during a telemedicine ortelehealth session, a recommendation pertaining to a treatment plan to acomputing device (e.g., patient interface 50, assistant interface 94, orany suitable interface described herein). The recommendation may bepresented on a display screen of the computing device in real-time(e.g., less than 2 seconds) in a portion of the display screen whileanother portion of the display screen presents video of a user (e.g.,patient, healthcare professional, or any suitable user). Therecommendation may also be presented on a display screen of thecomputing device in near time (e.g., preferably more than or equal to 2seconds and less than or equal to 10 seconds) or with a suitable timedelay necessary for the user of the display screen to be able to observethe display screen.

At 1004, the processing device may receive, from the computing device, aselection of the treatment plan. The user (e.g., patient, healthcareprofessional, assistant, etc.) may use any suitable input peripheral(e.g., mouse, keyboard, microphone, touchpad, etc.) to select therecommended treatment plan. The computing device may transmit theselection to the processing device of the server 30, which is configuredto receive the selection. There may any suitable number of treatmentplans presented on the display screen. Each of the treatment plansrecommended may provide different results and the healthcareprofessional may consult, during the telemedicine session, with theuser, to discuss which result the user desires. In some embodiments, therecommended treatment plans may only be presented on the computingdevice of the healthcare professional and not on the computing device ofthe user (patient interface 50). In some embodiments, the healthcareprofessional may choose an option presented on the assistant interface94. The option may cause the treatment plans to be transmitted to thepatient interface 50 for presentation. In this way, during thetelemedicine session, the healthcare professional and the user may viewthe treatment plans at the same time in real-time or in near real-time,which may provide for an enhanced user experience for the patient and/orhealthcare professional using the computing device.

After the selection of the treatment plan is received at the server 30,at 1006, while the user uses the treatment apparatus 70, the processingdevice may control, based on the selected treatment plan, the treatmentapparatus 70. In some embodiments, controlling the treatment apparatus70 may include the server 30 generating and transmitting controlinstructions to the treatment apparatus 70. In some embodiments,controlling the treatment apparatus 70 may include the server 30generating and transmitting control instructions to the patientinterface 50, and the patient interface 50 may transmit the controlinstructions to the treatment apparatus 70. The control instructions maycause an operating parameter (e.g., speed, orientation, required force,range of motion of pedals, etc.) to be dynamically changed according tothe treatment plan (e.g., a range of motion may be changed to a certainsetting based on the user achieving a certain range of motion for acertain period of time). The operating parameter may be dynamicallychanged while the patient uses the treatment apparatus 70 to perform anexercise. In some embodiments, during a telemedicine session between thepatient interface 50 and the assistant interface 94, the operatingparameter may be dynamically changed in real-time or near real-time.

FIG. 11 shows an example computer system 1100 which can perform any oneor more of the methods described herein, in accordance with one or moreaspects of the present disclosure. In one example, computer system 1100may include a computing device and correspond to the assistanceinterface 94, reporting interface 92, supervisory interface 90,clinician interface 20, server 30 (including the AI engine 11), patientinterface 50, ambulatory sensor 82, goniometer 84, treatment apparatus70, pressure sensor 86, or any suitable component of FIG. 1 , furtherthe computer system 1100 may include the computing device 1200 of FIG.12 . The computer system 1100 may be capable of executing instructionsimplementing the one or more machine learning models 13 of theartificial intelligence engine 11 of FIG. 1 . The computer system may beconnected (e.g., networked) to other computer systems in a LAN, anintranet, an extranet, or the Internet, including via the cloud or apeer-to-peer network. The computer system may operate in the capacity ofa server in a client-server network environment. The computer system maybe a personal computer (PC), a tablet computer, a wearable (e.g.,wristband), a set-top box (STB), a personal Digital Assistant (PDA), amobile phone, a camera, a video camera, an Internet of Things (IoT)device, or any device capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatdevice. Further, while only a single computer system is illustrated, theterm “computer” shall also be taken to include any collection ofcomputers that individually or jointly execute a set (or multiple sets)of instructions to perform any one or more of the methods discussedherein.

The computer system 1100 includes a processing device 1102, a mainmemory 1104 (e.g., read-only memory (ROM), flash memory, solid statedrives (SSDs), dynamic random-access memory (DRAM) such as synchronousDRAM (SDRAM)), a static memory 1106 (e.g., flash memory, solid statedrives (SSDs), static random access memory (SRAM)), and a data storagedevice 1108, which communicate with each other via a bus 1110.

Processing device 1102 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 1102 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 1102 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a system on a chip, a field programmable gate array (FPGA), adigital signal processor (DSP), network processor, or the like. Theprocessing device 1102 is configured to execute instructions forperforming any of the operations and steps discussed herein.

The computer system 1100 may further include a network interface device1112. The computer system 1100 also may include a video display 1114(e.g., a liquid crystal display (LCD), a light-emitting diode (LED), anorganic light-emitting diode (OLED), a quantum LED, a cathode ray tube(CRT), a shadow mask CRT, an aperture grille CRT, a monochrome CRT), oneor more input devices 1116 (e.g., a keyboard and/or a mouse or agaming-like control), and one or more speakers 1118 (e.g., a speaker).In one illustrative example, the video display 1114 and the inputdevice(s) 1116 may be combined into a single component or device (e.g.,an LCD touch screen).

The data storage device 1116 may include a computer-readable medium 1120on which the instructions 1122 embodying any one or more of the methods,operations, or functions described herein is stored. The instructions1122 may also reside, completely or at least partially, within the mainmemory 1104 and/or within the processing device 1102 during executionthereof by the computer system 1100. As such, the main memory 1104 andthe processing device 1102 also constitute computer-readable media. Theinstructions 1122 may further be transmitted or received over a networkvia the network interface device 1112.

While the computer-readable storage medium 1120 is shown in theillustrative examples to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

FIG. 12 generally illustrates a perspective view of a person using thetreatment apparatus 70, 100 of FIG. 2 , the patient interface 50, and acomputing device 1200 according to the principles of the presentdisclosure. In some embodiments, the patient interface 50 may not beable to communicate via a network to establish a telemedicine sessionwith the assistant interface 94. In such an instance the computingdevice 1200 may be used as a relay to receive cardiovascular data fromone or more sensors attached to the user and transmit the cardiovasculardata to the patient interface 50 (e.g., via Bluetooth), the server 30,and/or the assistant interface 94. The computing device 1200 may becommunicatively coupled to the one or more sensors via a short-rangewireless protocol (e.g., Bluetooth). In some embodiments, the computingdevice 1200 may be connected to the assistant interface via atelemedicine session. Accordingly, the computing device 1200 may includea display configured to present video of the healthcare professional, topresent instructional videos, to present treatment plans, etc. Further,the computing device 1200 may include a speaker configured to emit audiooutput, and a microphone configured to receive audio input (e.g.,microphone).

In some embodiments, the computing device 1200 may be a smartphonecapable of transmitting data via a cellular network and/or a wirelessnetwork. The computing device 1200 may include one or more memorydevices storing instructions that, when executed, cause one or moreprocessing devices to perform any of the methods described herein. Thecomputing device 1200 may have the same or similar components as thecomputer system 1100 in FIG. 11 .

In some embodiments, the treatment apparatus 70 may include one or morestands configured to secure the computing device 1200 and/or the patientinterface 50, such that the user can exercise hands-free.

In some embodiments, the computing device 1200 functions as a relaybetween the one or more sensors and a second computing device (e.g.,assistant interface 94) of a healthcare professional, and a thirdcomputing device (e.g., patient interface 50) is attached to thetreatment apparatus and presents, on the display, information pertainingto a treatment plan.

FIG. 13 generally illustrates a display 1300 of the computing device1200, and the display presents a treatment plan 1302 designed to improvethe user's cardiovascular health according to the principles of thepresent disclosure.

As depicted, the display 1300 only includes sections for the userprofile 130 and the video feed display 1308, including the self-videodisplay 1310. During a telemedicine session, the user may operate thecomputing device 1200 in connection with the assistant interface 94. Thecomputing device 1200 may present a video of the user in the self-video1310, wherein the presentation of the video of the user is in a portionof the display 1300 that also presents a video from the healthcareprofessional in the video feed display 1308. Further, the video feeddisplay 1308 may also include a graphical user interface (GUI) object1306 (e.g., a button) that enables the user to share with the healthcareprofessional on the assistant interface 94 in real-time or nearreal-time during the telemedicine session the recommended treatmentplans and/or excluded treatment plans. The user may select the GUIobject 1306 to select one of the recommended treatment plans. Asdepicted, another portion of the display 1300 may include the userprofile display 1300.

In FIG. 13 , the user profile display 1300 is presenting two examplerecommended treatment plans 1302 and one example excluded treatment plan1304. As described herein, the treatment plans may be recommended basedon a cardiovascular health issue of the user, a standardized measurecomprising perceived exertion, cardiovascular data of the user,attribute data of the user, feedback data from the user, and the like.In some embodiments, the one or more trained machine learning models 13may generate treatment plans that include exercises associated withincreasing the user's cardiovascular health by a certain threshold(e.g., any suitable percentage metric, value, percentage, number,indicator, probability, etc., which may be configurable). The trainedmachine learning models 13 may match the user to a certain cohort basedon a probability of likelihood that the user fits that cohort. Atreatment plan associated with that particular cohort may be prescribedfor the user, in some embodiments.

For example, as depicted, the user profile display 1300 presents “Yourcharacteristics match characteristics of users in Cohort A. Thefollowing treatment plans are recommended for you based on yourcharacteristics and desired results.” Then, the user profile display1300 presents a first recommended treatment plan. The treatment plansmay include any suitable number of exercise sessions for a user. Eachsession may be associated with a different exertion level for the userto achieve or to maintain for a certain period of time. In someembodiments, more than one session may be associated with the sameexertion level if having repeated sessions at the same exertion levelare determined to enhance the user's cardiovascular health. The exertionlevels may change dynamically between the exercise sessions based ondata (e.g., the cardiovascular health issue of the user, thestandardized measure of perceived exertion, cardiovascular data,attribute data, etc.) that indicates whether the user's cardiovascularhealth or some portion thereof is improving or deteriorating.

As depicted, treatment plan “1” indicates “Use treatment apparatus for 2sessions a day for 5 days to improve cardiovascular health. In the firstsession, you should use the treatment apparatus at a speed of 5 milesper hour for 20 minutes to achieve a minimal desired exertion level. Inthe second session, you should use the treatment apparatus at a speed of10 miles per hour 30 minutes a day for 4 days to achieve a high desiredexertion level. The prescribed exercise includes pedaling in a circularmotion profile.” This specific example and all such examples elsewhereherein are not intended to limit in any way the generated treatment planfrom recommending any suitable number of exercises and/or type(s) ofexercise.

As depicted, the patient profile display 1300 may also present theexcluded treatment plans 1304. These types of treatment plans are shownto the user by using the computing device 1200 to alert the user not toperform certain treatment plans that could potentially harm the user'scardiovascular health. For example, the excluded treatment plan couldspecify the following: “You should not use the treatment apparatus forlonger than 40 minutes a day due to a cardiovascular health issue.”Specifically, in this example, the excluded treatment plan points out alimitation of a treatment protocol where, due to a cardiovascular healthissue, the user should not exercise for more than 40 minutes a day.Excluded treatment plans may be based on results from other users havinga cardiovascular heart issue when performing the excluded treatmentplans, other users' cardiovascular data, other users' attributes, thestandardized measure of perceived exertion, or some combination thereof.

The user may select which treatment plan to initiate. For example, theuser may use an input peripheral (e.g., mouse, touchscreen, microphone,keyboard, etc.) to select from the treatment plans 1302.

In some embodiments, the recommended treatment plans and excludedtreatment plans may be presented on the display 120 of the assistantinterface 94. The assistant may select the treatment plan for the userto follow to achieve a desired result. The selected treatment plan maybe transmitted for presentation to the computing device 1200 and/or thepatient interface 50. The patient may view the selected treatment planon the computing device 1200 and/or patient interface 50. In someembodiments, the assistant and the patient may discuss the details(e.g., treatment protocol using treatment apparatus 70, diet regimen,medication regimen, etc.) during the telemedicine session in real-timeor in near real-time. In some embodiments, as the user uses thetreatment apparatus 70, as discussed further with reference to method1000 of FIG. 10 above, the server 30 may control, based on the selectedtreatment plan and during the telemedicine session, the treatmentapparatus 70.

FIG. 14 generally illustrates an example embodiment of a method 1400 forgenerating treatment plans, where such treatment plans may includesessions designed to enable a user, based on a standardized measure ofperceived exertion, to achieve a desired exertion level according to theprinciples of the present disclosure. The method 1400 may be performedby processing logic that may include hardware (circuitry, dedicatedlogic, etc.), software, or a combination of both. The method 1400 and/oreach of their individual functions, subroutines, or operations may beperformed by one or more processors of a computing device (e.g., thecomputing device 1200 of FIG. 12 and/or the patient interface 50 of FIG.1 ) implementing the method 1400. The method 1400 may be implemented ascomputer instructions stored on a memory device and executable by theone or more processors. In certain implementations, the method 1400 maybe performed by a single processing thread. Alternatively, the method1400 may be performed by two or more processing threads, each threadimplementing one or more individual functions, routines, subroutines, oroperations of the methods.

At block 1402, the processing device may receive, at a computing device1200, a first treatment plan designed to treat a cardiovascular healthissue of a user. The cardiovascular heart issue may include diagnoses,diagnostic codes, symptoms, life consequences, comorbidities, riskfactors to health, risk factors to life, etc. The cardiovascular heartissue may include heart surgery performed on the user, a hearttransplant performed on the user, a heart arrhythmia of the user, anatrial fibrillation of the user, tachycardia, bradycardia,supraventricular tachycardia, congestive heart failure, heart valvedisease, arteriosclerosis, atherosclerosis, pericardial disease,pericarditis, myocardial disease, myocarditis, cardiomyopathy,congenital heart disease, or some combination thereof.

The first treatment plan may include at least two exercise sessions thatprovide different exertion levels based at least on the cardiovascularhealth issue of the user. For example, if the user recently underwentheart surgery, then the user may be at high risk for a complication iftheir heart is overexerted. Accordingly, a first exercise session maybegin with a very mild desired exertion level, and a second exercisesession may slightly increase the exertion level. There may any suitablenumber of exercise sessions in an exercise protocol associated with thetreatment plan. The number of sessions may depend on the cardiovascularhealth issue of the user. For example, the person who recently underwentheart surgery may be prescribed a higher number of sessions (e.g., 36)than the number of sessions prescribed in a treatment plan to a personwith a less severe cardiovascular health issue. The first treatment planmay be presented on the display 1300 of the computing device 1200.

In some embodiments, the first treatment plan may also be generated byaccounting for a standardized measure comprising perceived exertion,such as a metabolic equivalent of task (MET) value and/or the BorgRating of Perceived Exertion (RPE). The MET value refers to an objectivemeasure of a ratio of the rate at which a person expends energy relativeto the mass of that person while performing a physical activity comparedto a reference (resting rate). In other words, MET may refer to a ratioof work metabolic rate to resting metabolic rate. One MET may be definedas 1 kcal/kg/hour and approximately the energy cost of sitting quietly.Alternatively, and without limitation, one MET may be defined as oxygenuptake in ml/kg/min where one MET is equal to the oxygen cost of sittingquietly (e.g., 3.5 ml/kg/min). In this example, 1 MET is the rate ofenergy expenditure at rest. A 5 MET activity expends 5 times the energyused when compared to the energy used for by a body at rest. Cycling maybe a 6 MET activity. If a user cycles for 30 minutes, then that isequivalent to 180 MET activity (i.e., 6 MET×30 minutes). Attainingcertain values of MET may be beneficial or detrimental for people havingcertain cardiovascular health issues.

A database may store a table including MET values for activitiescorrelated with treatment plans, cardiovascular results of users havingcertain cardiovascular health issues, and/or cardiovascular data. Thedatabase may be continuously and/or continually updated as data isobtained from users performing treatment plans. The database may be usedto train the one or more machine learning models such that improvedtreatment plans with exercises having certain MET values are selected.The improved treatment plans may result in faster cardiovascular healthrecovery time and/or a better cardiovascular health outcome. Theimproved treatment plans may result in reduced use of the treatmentapparatus, computing device 1200, patient interface 50, server 30,and/or assistant interface 94. Accordingly, the disclosed techniques mayreduce the resources (e.g., processing, memory, network) consumed by thetreatment apparatus, computing device 1200, patient interface 50, server30, and/or assistant interface 94, thereby providing a technicalimprovement. Further, wear and tear of the treatment apparatus,computing device 1200, patient interface 50, server 30, and/or assistantinterface 94 may be reduced, thereby improving their lifespan.

The Borg RPE is a standardized way to measure physical activityintensity level. Perceived exertion refers to how hard a person feelslike their body is working. The Borg RPE may be used to estimate auser's actual heartrate during physical activity. The Borg RPE may bebased on physical sensations a person experiences during physicalactivity, including increased heartrate, increased respiration orbreathing rate, increased sweating, and/or muscle fatigue. The Borgrating scale may be from 6 (no exertion at all) to 20 (perceivingmaximum exertion of effort). Similar to the MET table described above,the database may include a table that correlates the Borg values foractivities with treatment plans, cardiovascular results of users havingcertain cardiovascular health issues, and/or cardiovascular data.

In some embodiments, the first treatment plan may be generated by one ormore trained machine learning models. The machine learning models 13 maybe trained by training engine 9. The one or more trained machinelearning models may be trained using training data including labeledinputs of a standardized measure comprising perceived exertion, otherusers' cardiovascular data, attribute data of the user, and/or otherusers' cardiovascular health issues and a labeled output for a predictedtreatment plan (e.g., the treatment plans may include details related tothe number of exercise sessions, the exercises to perform at eachsession, the duration of the exercises, the exertion levels to maintainor achieve at each session, etc.). The attribute data may be received bythe processing device and may include an eating or drinking schedule ofthe user, information pertaining to an age of the user, informationpertaining to a sex of the user, information pertaining to a gender ofthe user, an indication of a mental state of the user, informationpertaining to a genetic condition of the user, information pertaining toa disease state of the user, information pertaining to a microbiome fromone or more locations on or in the user, an indication of an energylevel of the user, information pertaining to a weight of the user,information pertaining to a height of the user, information pertainingto a body mass index (BMI) of the user, information pertaining to afamily history of cardiovascular health issues of the user, informationpertaining to comorbidities of the user, information pertaining todesired health outcomes of the user if the treatment plan is followed,information pertaining to predicted health outcomes of the user if thetreatment plan is not followed, of some combination thereof.

A mapping function may be used to map, using supervised learning, thelabeled inputs to the labeled outputs, in some embodiments. In someembodiments, the machine learning models may be trained to output aprobability that may be used to match to a treatment plan or match to acohort of users that share characteristics similar to those of the user.If the user is matched to a cohort based on the probability, a treatmentplan associated with that cohort may be prescribed to the user.

In some embodiments, the one or more machine learning models may includedifferent layers of nodes that determine different outputs based ondifferent data. For example, a first layer may determine, based oncardiovascular data of the user, a first probability of a predictedtreatment plan. A second layer may receive the first probability anddetermine, based on the cardiovascular health issue of the user, asecond probability of the predicted treatment plan. A third layer mayreceive the second probability and determine, based on the standardizedmeasure of perceived exertion, a third probability of the predictedtreatment plan. An activation function may combine the output from thethird layer and output a final probability which may be used toprescribe the first treatment plan to the user.

In some embodiments, the first treatment plan may be designed andconfigured by a healthcare professional. In some embodiments, a hybridapproach may be used and the one or more machine learning models mayrecommend one or more treatment plans for the user and present them onthe assistant interface 94. The healthcare professional may select oneof the treatment plans, modify one of the treatment plans, or both, andthe first treatment plan may be transmitted to the computing device 1200and/or the patient interface 50.

At block 1404, while the user uses the treatment apparatus 70 to performthe first treatment plan for the user, the processing device may receivecardiovascular data from one or more sensors configured to measure thecardiovascular data associated with the user. In some embodiments, thetreatment apparatus may include a cycling machine. The one or moresensors may include an electrocardiogram sensor, a pulse oximeter, ablood pressure sensor, a respiration rate sensor, a spirometry sensor,or some combination thereof. The electrocardiogram sensor may be a straparound the user's chest, the pulse oximeter may be clip on the user'sfinger, and the blood pressure sensor may be cuff on the user's arm.Each of the sensors may be communicatively coupled with the computingdevice 1200 via Bluetooth or a similar near field communicationprotocol. The cardiovascular data may include a cardiac output of theuser, a heartrate of the user, a heart rhythm of the user, a bloodpressure of the user, a blood oxygen level of the user, a cardiovasculardiagnosis of the user, a non-cardiovascular diagnosis of the user, arespiration rate of the user, spirometry data related to the user, orsome combination thereof.

At block 1406, the processing device may transmit the cardiovasculardata. In some embodiments, the cardiovascular data may be transmitted tothe assistant interface 94 via the first network 34 and the secondnetwork 54. In some embodiments, the cardiovascular data may betransmitted to the server 30 via the second network 54. In someembodiments, cardiovascular data may be transmitted to the patientinterface 50 (e.g., second computing device) which relays thecardiovascular data to the server 30 via the second network 58. In someembodiments, cardiovascular data may be transmitted to the patientinterface 50 (e.g., second computing device) which relays thecardiovascular data to the assistant interface 94 (e.g., third computingdevice).

In some embodiments, one or more machine learning models 13 of theserver 30 may be used to generate a second treatment plan. The secondtreatment plan may modify at least one of the exertion levels, and themodification may be based on a standardized measure of perceivedexertion, the cardiovascular data, and the cardiovascular health issueof the user. In some embodiments, if the user is not able to meet ormaintain the exertion level for a session, the one or more machinelearning models 13 of the server 30 may modify the exertion leveldynamically.

At block 1408, the processing device may receive the second treatmentplan.

In some embodiments, the second treatment plan may include a modifiedparameter pertaining to the treatment apparatus 70. The modifiedparameter may include a resistance, a range of motion, a length of time,an angle of a component of the treatment apparatus, a speed, or somecombination thereof. In some embodiments, while the user operates thetreatment apparatus 70, the processing device may, based on the modifiedparameter in real-time or near real-time, cause the treatment apparatus70 to be controlled.

In some embodiments, the one or more machine learning models maygenerate the second treatment plan by predicting exercises that willresult in the desired exertion level for each session, and the one ormore machine learning models may be trained using data pertaining to thestandardized measure of perceived exertion, other users' cardiovasculardata, and other users' cardiovascular health issues.

At block 1410, the processing device may present the second treatmentplan on a display, such as the display 1300 of the computing device1200.

In some embodiments, based on an operating parameter specified in thetreatment plan, the second treatment plan, or both, the computing device1200, the patient interface 50, the server 30, and/or the assistantinterface 94 may send control instructions to control the treatmentapparatus 70. The operating parameter may pertain to a speed of a motorof the treatment apparatus 70, a range of motion provided by one or morepedals of the treatment apparatus 70, an amount of resistance providedby the treatment apparatus 70, or the like.

FIG. 15 generally illustrates an example embodiment of a method 1500 forreceiving input from a user and transmitting the feedback to be used togenerate a new treatment plan according to the principles of the presentdisclosure. The method 1500 may be performed by processing logic thatmay include hardware (circuitry, dedicated logic, etc.), software, or acombination of both. The method 1500 and/or each of their individualfunctions, subroutines, or operations may be performed by one or moreprocessors of a computing device (e.g., the computing device 1200 ofFIG. 12 and/or the patient interface 50 of FIG. 1 ) implementing themethod 1500. The method 1500 may be implemented as computer instructionsstored on a memory device and executable by the one or more processors.In certain implementations, the method 1500 may be performed by a singleprocessing thread. Alternatively, the method 1500 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods.

At block 1502, while the user uses the treatment apparatus 70 to performthe first treatment plan for the user, the processing device may receivefeedback from the user. The feedback may include input from amicrophone, a touchscreen, a keyboard, a mouse, a touchpad, a wearabledevice, the computing device, or some combination thereof. In someembodiments, the feedback may pertain to whether or not the user is inpain, whether the exercise is too easy or too hard, whether or not toincrease or decrease an operating parameter of the treatment apparatus70, or some combination thereof.

At block 1504, the processing device may transmit the feedback to theserver 30, wherein the one or more machine learning models uses thefeedback to generate the second treatment plan.

System and Method for Implementing a Cardiac Rehabilitation Protocol byUsing Artificial Intelligence and a Standardized Measurement

FIG. 16 generally illustrates an example embodiment of a method 1600 forimplementing a cardiac rehabilitation protocol by using artificialintelligence and a standardized measurement according to the principlesof the present disclosure. The method 1600 may be performed byprocessing logic that may include hardware (circuitry, dedicated logic,etc.), software, or a combination of both. The method 1600 and/or eachof their individual functions, subroutines, or operations may beperformed by one or more processing devices of a computing device (e.g.,the computer system 1100 of FIG. 11 ) implementing the method 1600. Themethod 1600 may be implemented as computer instructions stored on amemory device and executable by the one or more processing devices. Incertain implementations, the method 1600 may be performed by a singleprocessing thread. Alternatively, the method 1600 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods.

In some embodiments, a system may be used to implement the method 1600.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 1600.

At block 1602, the processing device may determine a maximum targetheartrate for a user using the electromechanical machine to perform thetreatment plan. In some embodiments, the processing device may determinethe maximum target heartrate by determining a heartrate reserve measure(HRRM) by subtracting from a maximum heartrate of the user a restingheartrate of the user.

At block 1604, the processing device may receive, via the interface(patient interface 50), an input pertaining to a perceived exertionlevel of the user. In some embodiments, the processing device mayreceive, via the interface, an input pertaining to a level of the user'sanxiety, depression, pain, difficulty in performing the treatment plan,or some combination thereof. In some embodiments, the processing devicemay receive, via the interface, an input pertaining to a physicalactivity readiness (PAR) score, and the processing device may determine,based on the PAR score, an initiation point at which the user is tobegin the treatment plan. The treatment plan may pertain to cardiacrehabilitation, bariatric rehabilitation, cardio-oncologicrehabilitation, oncologic rehabilitation, pulmonary rehabilitation, orsome combination thereof.

In some embodiments, the processing device may receive, from one or moresensors, performance data related to the user's performance of thetreatment plan. Based on the performance data, the input(s) receivedfrom the interface, or some combination thereof, the processing devicemay determine a state of the user.

At block 1606, based on the perceived exertion level and the maximumheartrate, the processing device may determine an amount of resistancefor the electromechanical machine to provide via one or more pedalsphysically or communicatively coupled to the electromechanical machine.In some embodiments, the processing device may use one or more trainedmachine learning models that map one or more inputs to one or moreoutputs, wherein the mapping is to determine the amount of resistancethe electromechanical machine is to provide via the one or more pedals.The one or more machine learning models 13 may be trained using atraining dataset. The training dataset may include labeled inputs mappedto labeled outputs. The labeled inputs may pertain to one or morecharacteristics of one or more users (e.g., maximum target heartrates ofusers, perceived exertion levels of users during exercises using certainamounts of resistance, physiological data of users, health conditions ofusers, etc.) mapped to labeled outputs including amounts of resistanceto provide by one or more pedals of an electromechanical machine.

At block 1608, while the user performs the treatment plan, theprocessing device may cause the electromechanical machine to provide theamount of resistance.

Further, in some embodiments, the processing device may transmit inreal-time or near real-time one or more characteristic data of the userto a computing device used by a healthcare professional. Thecharacteristic data may be transmitted to and presented on the computingdevice monitored by the healthcare professional. The characteristic datamay include measurement data, performance data, and/or personal datapertaining to the user. For example, one or more wireless sensors mayobtain the user's heartrate, blood pressure, blood oxygen level, and thelike at a certain frequency (e.g., every 5 minutes, every 2 minutes,every 30 seconds, etc.) and transmit those measurements to the computingdevice 1200 or the patient interface 50. The computing device 1200and/or patient interface 50 may relay the measurements to the server 30,which may transmit the measurements for real-time display on theassistant interface 94.

In some embodiments, the processing device may receive, via one or morewireless sensors (e.g., blood pressure cuff, electrocardiogram wirelesssensor, blood oxygen level sensor, etc.), one or more measurementsincluding a blood pressure, a heartrate, a respiration rate, a bloodoxygen level, or some combination thereof, in real-time or nearreal-time. In some embodiments, based on the one or more measurements,the processing device may determine whether the user's heartrate iswithin a threshold relative to the maximum target heartrate. In someembodiments, if the one or more measurements exceed the threshold, theprocessing device may reduce the amount of resistance provided by theelectromechanical machine. If the one or more measurements do not exceedthe threshold, the processing device may maintain the amount ofresistance provided by the electromechanical machine.

Clause 1.1 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user is performing a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   determine a maximum target heartrate for a user using the        electromechanical machine to perform the treatment plan;    -   receive, via the interface, an input pertaining to a perceived        exertion level of the user;    -   based on the perceived exertion level and the maximum heartrate,        determine an amount of resistance for the electromechanical        machine to provide via one or more pedals physically or        communicatively coupled to the electromechanical machine; and    -   while the user performs the treatment plan, cause the        electromechanical machine to provide the amount of resistance.

Clause 2.1 The computer-implemented system of any clause herein, whereinthe processing device is further to:

-   -   receive, via the interface, a second input pertaining to a level        of the user's anxiety, depression, pain, difficulty in        performing the treatment plan, or any combination thereof.

Clause 3.1 The computer-implemented system of any clause herein, whereinthe processing device is further to:

-   -   receive, via the interface, a second input pertaining to a        physical activity readiness (PAR) score; and    -   determine, based on the PAR, an initiation point at which the        user is to begin the treatment plan, wherein the treatment plan        pertains to cardiac rehabilitation.

Clause 4.1 The computer-implemented system of any clause herein, furthercomprising:

-   -   receiving, from one or more sensors, performance data related to        the user's performance of the treatment plan; and    -   based on the performance data, the input, the second input, or        some combination thereof, determining a state of the user.

Clause 5.1 The computer-implemented system of any clause herein, whereinthe processing device is further to transmit in real-time or nearreal-time one or more characteristic data of the user to a computingdevice used by a healthcare professional, wherein the characteristicdata is transmitted to and presented on the computing device monitoredby the healthcare professional.

Clause 6.1 The computer-implemented system of any clause herein, whereinthe processing device is further to determine the maximum targetheartrate by:

determining a heartrate reserve measure (HRRM) by subtracting from amaximum heartrate of the user a resting heartrate of the user.

Clause 7.1 The computer-implemented system of any clause herein, whereinthe processing device is further to use one or more trained machinelearning models that map one or more inputs to one or more outputs,wherein the mapping is to determine the amount of resistance theelectromechanical machine is to provide via the one or more pedals.

Clause 8.1 The computer-implemented system of any clause herein, whereinthe processing device is further to:

-   -   receive, via one or more sensors, one or more measurements        comprising a blood pressure, a heartrate, a respiration rate, a        blood oxygen level, or some combination thereof, in real-time or        near real-time;    -   based on the one or more measurements, determine whether the        user's heartrate is within a threshold relative to the maximum        target heartrate; and    -   if the one or more measurements exceed the threshold, reduce the        amount of resistance provided by the electromechanical machine.

Clause 9.1 A computer-implemented method comprising:

-   -   determining a maximum target heartrate for a user using an        electromechanical machine to perform a treatment plan, wherein        the electromechanical machine is configured to be manipulated by        the user while the user is performing the treatment plan;    -   receiving, via an interface, an input pertaining to a perceived        exertion level of the user, wherein the interface comprises a        display configured to present information pertaining to the        treatment plan;    -   based on the perceived exertion level and the maximum heartrate,        determining an amount of resistance for the electromechanical        machine to provide via one or more pedals physically or        communicatively coupled to the electromechanical machine; and    -   while the user performs the treatment plan, causing the        electromechanical machine to provide the amount of resistance.

Clause 10.1 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, via the interface, a second input pertaining to a        level of the user's anxiety, depression, pain, difficulty in        performing the treatment plan, or any combination thereof.

Clause 11.1 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, via the interface, a second input pertaining to a        physical activity readiness (PAR) score; and    -   determining, based on the PAR, an initiation point at which the        user is to begin the treatment plan, wherein the treatment plan        pertains to cardiac rehabilitation.

Clause 12.1 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, from one or more sensors, performance data related to        the user's performance of the treatment plan; and    -   based on the performance data, the input, the second input, or        some combination thereof, determining a state of the user.

Clause 13.1 The computer-implemented method of any clause herein,further comprising transmitting in real-time or near real-time one ormore characteristic data of the user to a computing device used by ahealthcare professional, wherein the characteristic data is transmittedto and presented on the computing device monitored by the healthcareprofessional.

Clause 14.1 The computer-implemented method of any clause herein,wherein determining the maximum target heartrate further comprises:

-   -   determining a heartrate reserve measure (HRRM) by subtracting        from a maximum heartrate of the user a resting heartrate of the        user.

Clause 15.1 The computer-implemented method of any clause herein,further comprising using one or more trained machine learning modelsthat map one or more inputs to one or more outputs, wherein the mappingis to determine the amount of resistance the electromechanical machineis to provide via the one or more pedals.

Clause 16.1 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, via one or more sensors, one or more measurements        comprising a blood pressure, a heartrate, a respiration rate, a        blood oxygen level, or some combination thereof, in real-time or        near real-time;    -   based on the one or more measurements, determining whether the        user's heartrate is within a threshold relative to the maximum        target heartrate; and    -   if the one or more measurements exceed the threshold, reducing        the amount of resistance provided by the electromechanical        machine.

Clause 17.1 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, via the interface, a second input pertaining to a        level of the user's anxiety, depression, pain, difficulty in        performing the treatment plan, or any combination thereof.

Clause 18.1 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, via the interface, a second input pertaining to a        physical activity readiness (PAR) score; and    -   determining, based on the PAR, an initiation point at which the        user is to begin the treatment plan, wherein the treatment plan        pertains to cardiac rehabilitation.

Clause 19.1 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   determine a maximum target heartrate for a user using an        electromechanical machine to perform a treatment plan, wherein        the electromechanical machine is configured to be manipulated by        the user while the user is performing the treatment plan;    -   receive, via an interface, an input pertaining to a perceived        exertion level of the user, wherein the interface comprises a        display configured to present information pertaining to the        treatment plan;    -   based on the perceived exertion level and the maximum heartrate,        determine an amount of resistance for the electromechanical        machine to provide via one or more pedals physically or        communicatively coupled to the electromechanical machine; and    -   while the user performs the treatment plan, cause the        electromechanical machine to provide the amount of resistance.

Clause 20.1 The computer-readable medium of any clause herein, whereinthe processing device is further to:

-   -   receive, via the interface, a second input pertaining to a level        of the user's anxiety, depression, pain, difficulty in        performing the treatment plan, or any combination thereof.

System and Method to Enable Communication Detection Between Devices andPerformance of a Preventative Action

FIG. 17 generally illustrates an example embodiment of a method 1700 forenabling communication detection between devices and performance of apreventative action according to the principles of the presentdisclosure. The method 1700 may be performed by processing logic thatmay include hardware (circuitry, dedicated logic, etc.), software, or acombination of both. The method 1700 and/or each of their individualfunctions, subroutines, or operations may be performed by one or moreprocessing devices of a computing device (e.g., the computer system 1100of FIG. 11 ) implementing the method 1700. The method 1700 may beimplemented as computer instructions stored on a memory device andexecutable by the one or more processing devices. In certainimplementations, the method 1700 may be performed by a single processingthread. Alternatively, the method 1700 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 1700.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 1700.

At block 1702, the processing device may determine whether one or moremessages are received. The one or more messages may be received from theelectromechanical machine, one or more sensors, the patient interface50, the computing device 1200, or some combination thereof. The one ormore messages may include information pertaining to the user, the user'susage of the electromechanical machine, or both.

At block 1704, responsive to determining that the one or more messageshave not been received, the processing device may determine, via one ormore machine learning models 13, one or more preventative actions toperform. In some embodiments, the one or more messages not beingreceived may pertain to a telecommunications failure, a videocommunication being lost, an audio communication being lost, dataacquisition being compromised, or some combination thereof.

At block 1706, the processing device may cause the one or morepreventative actions to be performed. In some embodiments, the one ormore preventative actions may include causing a telecommunicationstransmission to be initiated (e.g., a phone call, a text message, avoice message, a video/multimedia message, a 911 call, a beaconactivation, a wireless communication of any kind, etc.), stopping theelectromechanical machine from operating, modifying a speed at which theelectromechanical machine operates, or some combination thereof.

In some embodiments, the one or more messages include informationpertaining to a cardiac health of the user, and the one or more messagesare sent by the electromechanical machine, the computing device 1200,the patient interface 50, the sensors, etc. while the user uses theelectromechanical machine to perform the treatment plan.

In some embodiments, the processing device may determine a maximumtarget heartrate for a user using the electromechanical machine toperform the treatment plan. The processing device may receive, via theinterface (patient interface 50), an input pertaining to a perceivedexertion level of the user. In some embodiments, based on the perceivedexertion level and the maximum target heartrate, the processing devicemay determine an amount of resistance for the electromechanical machineto provide via one or more pedals. While the user performs the treatmentplan, the processing device may cause the electromechanical machine toprovide the amount of resistance.

In some embodiments, the processing device may determine a conditionassociated with the user. The condition may pertain to cardiacrehabilitation, oncology rehabilitation, rehabilitation from pathologiesrelated to the prostate gland or urogenital tract, pulmonaryrehabilitation, bariatric rehabilitation, a wellness condition, ageneral state of the user based on vitals, physiologic data,measurements, or some combination thereof. Based on the conditionassociated with the user and the one or more messages not beingreceived, the processing device may determine the one or morepreventative actions. For example, if the one or more messages is notreceived and the user has a cardiac condition (e.g., abnormal heartrhythm), the preventative action may include stopping theelectromechanical machine and/or contact emergency services (e.g.,calling 911).

Clauses

Clause 1.2 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user is performing a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   determine whether the one or more messages are received, wherein        the one or more messages are received from the electromechanical        machine, a sensor, the interface, or some combination thereof,        and the one or more messages pertain to the user, the user's        usage of the electromechanical machine, or both;    -   responsive to determining that the one or more messages have not        been received, determining, via one or more machine learning        models, one or more preventative actions to perform; and    -   cause the one or more preventative actions to be performed.

Clause 2.2 The computer-implemented system of any clause herein, whereinthe one or more preventative actions comprise causing atelecommunications transmission to be initiated, stopping theelectromechanical machine from operating, modifying a speed at which theelectromechanical machine operates, or some combination thereof.

Clause 3.2 The computer-implemented system of any clause herein, whereinthe one or more messages not being received pertains to atelecommunications failure, a video communication being lost, an audiocommunication being lost, data acquisition being compromised, or somecombination thereof.

Clause 4.2 The computer-implemented system of any clause herein,wherein, while the user uses the electromechanical machine to performthe treatment plan, the one or more messages include informationpertaining to a cardiac health of the user.

Clause 5.2 The computer-implemented system of any clause herein, whereinthe processing device is to:

-   -   determine a maximum target heartrate for a user using the        electromechanical machine to perform the treatment plan;    -   receive, via the interface, an input pertaining to a perceived        exertion level of the user;    -   based on the perceived exertion level and the maximum heartrate,        determine an amount of resistance for the electromechanical        machine to provide via one or more pedals;    -   while the user performs the treatment plan, cause the        electromechanical machine to provide the amount of resistance.

Clause 6.2 The computer-implemented system of any clause herein, whereinthe processing device is further to:

-   -   determine a condition associated with the user; and    -   based on the condition associated with the user and the one or        more messages not being received, determining the one or more        preventative actions.

Clause 7.2 The computer-implemented system of any clause herein, whereinthe condition pertains to cardiac rehabilitation, oncologyrehabilitation, rehabilitation from pathologies related to the prostategland or urogenital tract, pulmonary rehabilitation, bariatricrehabilitation, or some combination thereof.

Clause 8.2 A computer-implemented method comprising:

-   -   determine whether one or more messages are received, wherein the        one or more messages are received from an electromechanical        machine, a sensor, the interface, or some combination thereof,        and the one or more messages pertain to a user, the user's usage        of the electromechanical machine, or both, and wherein the        electromechanical machine is configured to be manipulated by the        user while the user is performing a treatment plan;    -   responsive to determining that the one or more messages have not        been received, determine, via one or more machine learning        models, one or more preventative actions to perform; and    -   cause the one or more preventative actions to be performed.

Clause 9.2 The computer-implemented method of any clause herein, whereinthe one or more preventative actions comprise causing atelecommunications transmission to be initiated, stopping theelectromechanical machine from operating, modifying a speed at which theelectromechanical machine operates, or some combination thereof.

Clause 10.2 The computer-implemented method of any clause herein,wherein the one or more messages not being received pertains to atelecommunications failure, a video communication being lost, an audiocommunication being lost, data acquisition being compromised, or somecombination thereof.

Clause 11.2 The computer-implemented method of any clause herein,wherein, while the user uses the electromechanical machine to performthe treatment plan, the one or more messages include informationpertaining to a cardiac health of the user.

Clause 12.2 The computer-implemented method of any clause herein,wherein the processing device is to:

-   -   determine a maximum target heartrate for a user using the        electromechanical machine to perform the treatment plan;    -   receive, via an interface, an input pertaining to a perceived        exertion level of the user;    -   based on the perceived exertion level and the maximum heartrate,        determine an amount of resistance for the electromechanical        machine to provide via one or more pedals;    -   while the user performs the treatment plan, cause the        electromechanical machine to provide the amount of resistance.

Clause 13.2 The computer-implemented method of any clause herein,wherein the processing device is further to:

-   -   determine a condition associated with the user; and    -   based on the condition associated with the user and the one or        more messages not being received, determining the one or more        preventative actions.

Clause 14.2 The computer-implemented method of any clause herein,wherein the condition pertains to cardiac rehabilitation, oncologyrehabilitation, rehabilitation from pathologies related to the prostategland or urogenital tract, pulmonary rehabilitation, bariatricrehabilitation, or some combination thereof.

Clause 15.2 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   determine whether one or more messages are received, wherein the        one or more messages are received from an electromechanical        machine, a sensor, the interface, or some combination thereof,        and the one or more messages pertain to a user, the user's usage        of the electromechanical machine, or both, and wherein the        electromechanical machine is configured to be manipulated by a        user while the user is performing a treatment plan;    -   responsive to determining that the one or more messages have not        been received, determine, via one or more machine learning        models, one or more preventative actions to perform; and    -   cause the one or more preventative actions to be performed.

Clause 16.2 The computer-readable medium of any clause herein, whereinthe one or more preventative actions comprise causing atelecommunications transmission to be initiated, stopping theelectromechanical machine from operating, modifying a speed at which theelectromechanical machine operates, or some combination thereof.

Clause 17.2 The computer-readable medium of any clause herein, whereinthe one or more messages not being received pertains to atelecommunications failure, a video communication being lost, an audiocommunication being lost, data acquisition being compromised, or somecombination thereof.

Clause 18.2 The computer-readable medium of any clause herein, wherein,while the user uses the electromechanical machine to perform thetreatment plan, the one or more messages include information pertainingto a cardiac health of the user.

Clause 19.2 The computer-readable medium of any clause herein, whereinthe processing device is to:

-   -   determine a maximum target heartrate for a user using the        electromechanical machine to perform the treatment plan;    -   receive, via an interface, an input pertaining to a perceived        exertion level of the user;    -   based on the perceived exertion level and the maximum heartrate,        determine an amount of resistance for the electromechanical        machine to provide via one or more pedals;    -   while the user performs the treatment plan, cause the        electromechanical machine to provide the amount of resistance.

Clause 20.2 The computer-readable medium of any clause herein, whereinthe processing device is further to:

-   -   determine a condition associated with the user; and    -   based on the condition associated with the user and the one or        more messages not being received, determining the one or more        preventative actions.

System and Method for Using AI/ML to Detect Abnormal Heart Rhythms of aUser Performing a Treatment Plan Via an Electromechanical Machine

FIG. 18 generally illustrates an example embodiment of a method 1800 forusing artificial intelligence and machine learning to detect abnormalheart rhythms of a user performing a treatment plan via anelectromechanical machine according to the principles of the presentdisclosure. The method 1800 may be performed by processing logic thatmay include hardware (circuitry, dedicated logic, etc.), software, or acombination of both. The method 1800 and/or each of their individualfunctions, subroutines, or operations may be performed by one or moreprocessing devices of a computing device (e.g., the computer system 1100of FIG. 11 ) implementing the method 1800. The method 1800 may beimplemented as computer instructions stored on a memory device andexecutable by the one or more processing devices. In certainimplementations, the method 1800 may be performed by a single processingthread. Alternatively, the method 1800 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 1800.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 1800.

At block 1802, the processing device may receive, from one or moresensors, one or more measurements associated with the user, wherein theone or more measurements are received while the user performs thetreatment plan. In some embodiments, the one or more sensors may includea pulse oximeter, an electrocardiogram sensor, a heartrate sensor, ablood pressure sensor, a force sensor, or some combination thereof. Insome embodiments, each of the sensors may be wireless and may be enabledto communicate via a wireless protocol, such as Bluetooth.

At block 1804, the processing device may determine, based on one or morestandardized algorithms, a probability that the one or more measurementsare indicative of the user satisfying a threshold for a condition. Insome embodiments, the condition may include atrial fibrillation, atrialflutter, supraventricular tachycardia, ventricular fibrillation,ventricular tachycardia, any other abnormal heart rhythm, or somecombination thereof. In some embodiments, the one or more standardizedalgorithms may be approved by a government agency (e.g., Food and DrugAdministration), a regulatory agency, a non-governmental organization(NGO) or a standards body or organization.

The determining may be performed via one or more machine learning modelsexecuted by the processing device. The one or more machine learningmodels may be trained to determine a probability that the user satisfiesthe threshold for the condition. The one or more machine learning modelsmay include one or more hidden layers that each determine a respectiveprobability that are combined (e.g., summed, averaged, multiplied, etc.)in an activation function in a final layer of the machine learningmodel. The hidden layers may receive the one or more measurements, whichmay include a vital sign, a respiration rate, a heartrate, atemperature, a blood pressure, a glucose level, arterial blood gasand/or oxygenation levels or percentages, or other biomarker, or somecombination thereof. In some embodiments, the one or more machinelearning models may also receive performance information as input andthe performance information may include an elapsed time of using atreatment apparatus, an amount of force exerted on a portion of thetreatment apparatus, a range of motion achieved on the treatmentapparatus, a movement speed of a portion of the treatment apparatus, aduration of use of the treatment apparatus, an indication of a pluralityof pain levels using the treatment apparatus, or some combinationthereof. In some embodiments, the one or more machine learning modelsmay include personal information as input and the personal informationmay include demographic, psychographic or other information, such as anage, a weight, a gender, a height, a body mass index, a medicalcondition, a familial medication history, an injury, a medicalprocedure, a medication prescribed, or some combination thereof.

The one or more machine learning models may be trained with trainingdata that includes labeled inputs mapped to labeled outputs. The labeledinputs may include other users' measurement information, personalinformation, and/or performance information mapped to one or moreoutputs labeled as one or more conditions associated with the users.Further, the one or more machine learning models may be trained toimplement a standardized algorithm (e.g., photoplethysmographyalgorithm) approved by the Food and Drug Administration (FDA) to detectatrial fibrillation (AFib). The algorithm implemented by the machinelearning models may determine changes in blood volume based on themeasurements (e.g., heartrate, blood pressure, and/or blood vesselexpansion and contraction).

The threshold condition may be satisfied when one or more of themeasurements, alone or in combination, exceed a certain value. Forexample, if the user's heartrate is outside of 60 to 100 beat perminute, the machine learning model may determine a high probability theuser may be experiencing a heart attack and cause a preventative actionto be performed, such as initiating a telecommunication transmission(e.g., calling 911) and/or stopping the electromechanical machine. Themachine learning models may determine a high probability of heartarrhythmia when the heartrate is above 100 beats per minute or below 60beats per minute. Further, inputs received from the user may be used bythe machine learning models to determine whether the threshold issatisfied. For example, the inputs from the user may relate to whetherthe user is experiencing a fluttering sensation in the chest area or askipping of a heart beat.

At block 1806, responsive to determining that the one or moremeasurements indicate the user satisfies the threshold for thecondition, the processing device may perform one or more preventativeactions. In some embodiments, the one or more preventative actions mayinclude modifying an operating parameter of the electromechanicalmachine, presenting information on the interface, or some combinationthereof. In some embodiments, the processing device may alert, via theinterface, that the user has satisfied the threshold for the conditionand provide an instruction to modify usage of the electromechanicalmachine. In some embodiments, the one or more preventative actions mayinclude initiating a telemedicine session with a computing deviceassociated with a healthcare professional.

Clauses

Clause 1.3 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user is performing a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan;    -   determine, based on one or more standardized algorithms, a        probability that the one or more measurements are indicative of        the user satisfying a threshold for a condition, wherein the        determining is performed via one or more machine learning models        trained to determine a probability that the user satisfies the        threshold for the condition; and    -   responsive to determining that the one or more measurements        indicate the user satisfies the threshold for the condition,        perform one or more preventative actions.

Clause 2.3 The computer-implemented system of any clause herein, whereinthe one or more preventative actions comprise modifying an operatingparameter of the electromechanical machine, presenting information onthe interface, or some combination thereof.

Clause 3.3 The computer-implemented system of any clause herein, whereinthe condition comprises atrial fibrillation, atrial flutter,supraventricular tachycardia, ventricular fibrillation, ventriculartachycardia, any other abnormal heart rhythm, or some combinationthereof.

Clause 4.3 The computer-implemented system of any clause herein, whereinthe one or more sensors comprise a pulse oximeter, an electrocardiogramsensor, a heartrate sensor, a blood pressure sensor, a force sensor, orsome combination thereof.

Clause 5.3 The computer-implemented system of any clause herein, whereinthe processing device is further to:

-   -   determine whether the one or more messages have been received,        wherein the one or more messages have been received from the        electromechanical machine, a sensor, the interface, or some        combination thereof, and the one or more messages pertain to the        user, usage of the electromechanical machine, or both;    -   responsive to determining that the one or more messages have not        been received, determining, via one or more machine learning        models, one or more preventative actions to perform; and    -   cause the one or more preventative actions to be performed.

Clause 6.3 The computer-implemented system of any clause herein, whereinthe one or more standardized algorithms are approved by a governmentagency, a regulatory agency, a non-governmental organization (NGO) or astandards body or organization.

Clause 7.3 The computer-implemented system of any clause herein, whereinthe one or more preventative actions comprise initiating a telemedicinesession with a computing device associated with a healthcareprofessional.

Clause 8.3 A computer-implemented method comprising:

-   -   receiving, from one or more sensors, one or more measurements        associated with a user, wherein the one or more measurements are        received while the user performs a treatment plan, wherein an        electromechanical machine is configured to be manipulated by the        user while the user is performing the treatment plan;    -   determining, based on one or more standardized algorithms, a        probability that the one or more measurements are indicative of        the user satisfying a threshold for a condition, wherein the        determining is performed via one or more machine learning models        trained to determine a probability that the user satisfies the        threshold for the condition; and    -   responsive to determining that the one or more measurements        indicate the user satisfies the threshold for the condition,        performing one or more preventative actions.

Clause 9.3 The computer-implemented method of any clause herein, whereinthe one or more preventative actions comprise modifying an operatingparameter of the electromechanical machine, presenting information on aninterface, or some combination thereof.

Clause 10.3 The computer-implemented method of any clause herein,wherein the condition comprises atrial fibrillation, atrial flutter,supraventricular tachycardia, ventricular fibrillation, ventriculartachycardia, any other abnormal heart rhythm, or some combinationthereof.

Clause 11.3 The computer-implemented method of any clause herein,wherein the one or more sensors comprise a pulse oximeter, anelectrocardiogram sensor, a heartrate sensor, a blood pressure sensor, aforce sensor, or some combination thereof.

Clause 12.3 The computer-implemented method of any clause herein,further comprising:

-   -   determining whether the one or more messages have been received,        wherein the one or more messages have been received from the        electromechanical machine, a sensor, the interface, or some        combination thereof, and the one or more messages pertain to the        user, the user's usage of the electromechanical machine, or        both;    -   responsive to determining that the one or more messages have not        been received, determining, via one or more machine learning        models, one or more preventative actions to perform; and    -   causing the one or more preventative actions to be performed.

Clause 13.3 The computer-implemented method of any clause herein,wherein the one or more standardized algorithms are approved by agovernment agency, a regulatory agency, a non-governmental organization(NGO) or a standards body or organization.

Clause 14.3 The computer-implemented method of any clause herein,wherein the one or more preventative actions comprise initiating atelemedicine session with a computing device associated with ahealthcare professional.

Clause 15.3 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive, from one or more sensors, one or more measurements        associated with a user, wherein the one or more measurements are        received while the user performs a treatment plan, wherein an        electromechanical machine is configured to be manipulated by the        user while the user is performing the treatment plan;    -   determine, based on one or more standardized algorithms, a        probability that the one or more measurements are indicative of        the user satisfying a threshold for a condition, wherein the        determining is performed via one or more machine learning models        trained to determine a probability that the user satisfies the        threshold for the condition; and    -   responsive to determining that the one or more measurements        indicate the user satisfies the threshold for the condition,        performing one or more preventative actions.

Clause 16.3 The computer-readable medium of any clause herein, whereinthe one or more preventative actions comprise modifying an operatingparameter of the electromechanical machine, presenting information on aninterface, or some combination thereof.

Clause 17.3 The computer-readable medium of any clause herein, whereinthe condition comprises atrial fibrillation, atrial flutter,supraventricular tachycardia, ventricular fibrillation, ventriculartachycardia, any other abnormal heart rhythm, or some combinationthereof.

Clause 18.3 The computer-readable medium of any clause herein, whereinthe one or more sensors comprise a pulse oximeter, an electrocardiogramsensor, a heartrate sensor, a blood pressure sensor, a force sensor, orsome combination thereof.

Clause 19.3 The computer-readable medium of any clause herein, whereinthe processing device is further to:

-   -   determine whether the one or more messages have been received,        wherein the one or more messages have been received from the        electromechanical machine, a sensor, the interface, or some        combination thereof, and the one or more messages pertain to the        user, the user's usage of the electromechanical machine, or        both;    -   responsive to determining that the one or more messages have not        been received, determining, via one or more machine learning        models, one or more preventative actions to perform; and    -   cause the one or more preventative actions to be performed.

20.3 The computer-readable medium of any clause herein, wherein the oneor more standardized algorithms are approved by a government agency, aregulatory agency, a non-governmental organization (NGO) or a standardsbody or organization.

System and Method for Residentially-Based Cardiac Rehabilitation byUsing an Electromechanical Machine and Educational Content to MitigateRisk Factors and Optimize User Behavior

FIG. 19 generally illustrates an example embodiment of a method 1900 forresidentially-based cardiac rehabilitation by using an electromechanicalmachine and educational content to mitigate risk factors and optimizeuser behavior according to the principles of the present disclosure. Themethod 1900 may be performed by processing logic that may includehardware (circuitry, dedicated logic, etc.), software, or a combinationof both. The method 1900 and/or each of their individual functions,subroutines, or operations may be performed by one or more processingdevices of a computing device (e.g., the computer system 1100 of FIG. 11) implementing the method 1900. The method 1900 may be implemented ascomputer instructions stored on a memory device and executable by theone or more processing devices. In certain implementations, the method1900 may be performed by a single processing thread. Alternatively, themethod 1900 may be performed by two or more processing threads, eachthread implementing one or more individual functions, routines,subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 1900.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 1900.

At block 1902, the processing device may receive, from one or moresensors, one or more measurements associated with the user. The one ormore measurements may be received while the user performs the treatmentplan using the electromechanical machine. In some embodiments, theelectromechanical machine may include at least one of a cycling machine,a rowing machine, a stair-climbing machine, a treadmill, and anelliptical machine.

At block 1904, the processing device may determine, via one or moremachine learning models, one or more content items to present to theuser, wherein the determining is based on the one or more measurementsand one or more characteristics of the user. In some embodiments, theone or more content items may pertain to cardiac rehabilitation,oncology rehabilitation, rehabilitation from pathologies related to theprostate gland or urogenital tract, pulmonary rehabilitation, bariatricrehabilitation, or some combination thereof. In some embodiments, theprocessing device may modify one or more risk factors of the user bypresenting the one or more content items. The one or more risk factorsmay relate to cholesterol, blood pressure, stress, tobacco cessation,diabetes, or some combination thereof. In some embodiments, the riskfactors may relate to medication adherence of the user, as well asimprovements in the user's quality of life.

At block 1906, while the user performs the treatment plan using theelectromechanical machine, the processing device may cause presentationof the one or more content items on an interface. The one or morecontent items may include at least information related to a state of theuser, and the state of the user may be associated with the one or moremeasurements, the one or more characteristics, or some combinationthereof.

In some embodiments, the processing device may receive, from one or moreperipheral devices, input from the user. The input from the user mayinclude a request to view more details related to the information, arequest to receive different information, a request to receive relatedor complementary information, a request to stop presentation of theinformation, or some combination thereof.

In some embodiments, based on the one or more content items, theprocessing device may modify one or more operating parameters of theelectromechanical machine. Further, in some embodiments, based on usageof the electromechanical machine by the user, the processing device maymodify, in real-time or near real-time, playback of the one or morecontent items. For example, if the user has used the electromechanicalmachine for more than a threshold period of time, for more than athreshold number of times, or the like, then the processing device mayselect content items that are more relevant to a physical, emotional,mental, etc. state of the user relative to the usage of theelectromechanical machine. In other words, the processing device mayselect more content items including more advanced subject matter as theuser progresses in the treatment plan.

Clauses

Clause 1.4 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user is performing a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan;    -   determine, via one or more machine learning models, one or more        content items to present to the user, wherein the determining is        based on the one or more measurements and one or more        characteristics of the user; and    -   while the user performs the treatment plan using the        electromechanical machine, cause presentation of the one or more        content items on the interface, wherein the one or more content        items comprise at least information related to a state of the        user, and the state of the user is associated with the one or        more measurements, the one or more characteristics, or some        combination thereof.

Clause 2.4 The computer-implemented system of any clause herein, whereinthe one or more content items pertain to cardiac rehabilitation,oncology rehabilitation, rehabilitation from pathologies related to theprostate gland or urogenital tract, pulmonary rehabilitation, bariatricrehabilitation, or some combination thereof.

Clause 3.4 The computer-implemented system of any clause herein, whereinthe electromechanical machine is at least one of a cycling machine, arowing machine, and a stair-climbing machine, a treadmill, an andelliptical machine.

Clause 4.4 The computer-implemented system of any clause herein, whereinthe processing device is further to modify one or more risk factors ofthe user by presenting the one or more content items.

Clause 5.5 The computer-implemented system of any clause herein, whereinthe processing device is further to:

-   -   receive, from one or more peripheral devices, input from the        user, wherein the input from the user comprises a request to        view more details related to the information, a request to        receive different information, a request to receive related or        complementary information, a request to stop presentation of the        information, or some combination thereof.

Clause 6.4 The computer-implemented system of any clause herein,wherein, based on the one or more content items, the processing deviceis further to modify one or more operating parameters of theelectromechanical machine.

Clause 7.4 The computer-implemented system of any clause herein,wherein, based on usage of the electromechanical machine by the user,the processing device is further configured to modify in real-time ornear real-time playback of the one or more content items.

Clause 8.4 A computer-implemented method comprising:

-   -   receiving, from one or more sensors, one or more measurements        associated with a user, wherein the one or more measurements are        received while the user performs a treatment plan, and an        electromechanical machine is configured to be manipulated by the        user while the user is performing the treatment plan;    -   determining, via one or more machine learning models, one or        more content items to present to the user, wherein the        determining is based on the one or more measurements and one or        more characteristics of the user; and    -   while the user performs the treatment plan using the        electromechanical machine, causing presentation of the one or        more content items on an interface, wherein the one or more        content items comprise at least information related to a state        of the user, and the state of the user is associated with the        one or more measurements, the one or more characteristics, or        some combination thereof.

Clause 9.4 The computer-implemented method of any clause herein, whereinthe one or more content items pertain to cardiac rehabilitation,oncology rehabilitation, rehabilitation from pathologies related to theprostate gland or urogenital tract, pulmonary rehabilitation, bariatricrehabilitation, or some combination thereof.

Clause 10.4 The computer-implemented method of any clause herein,wherein the electromechanical machine is at least one of a cyclingmachine, a rowing machine, and a stair-climbing machine, a treadmill, anand elliptical machine.

Clause 11.4 The computer-implemented method of any clause herein,further comprising modifying one or more risk factors of the user bypresenting the one or more content items.

Clause 12.4 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, from one or more peripheral devices, input from the        user, wherein the input from the user comprises a request to        view more details related to the information, a request to        receive different information, a request to receive related or        complementary information, a request to stop presentation of the        information, or some combination thereof.

Clause 13.4 The computer-implemented method of any clause herein,further comprising, based on the one or more content items, modifyingone or more operating parameters of the electromechanical machine.

Clause 14.4 The computer-implemented method of any clause herein,further comprising, based on usage of the electromechanical machine bythe user, modifying in real-time or near real-time playback of the oneor more content items.

Clause 15. A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive, from one or more sensors, one or more measurements        associated with a user, wherein the one or more measurements are        received while the user performs a treatment plan, and an        electromechanical machine is configured to be manipulated by the        user while the user is performing the treatment plan;    -   determine, via one or more machine learning models, one or more        content items to present to the user, wherein the determining is        based on the one or more measurements and one or more        characteristics of the user; and    -   while the user performs the treatment plan using the        electromechanical machine, cause presentation of the one or more        content items on an interface, wherein the one or more content        items comprise at least information related to a state of the        user, and the state of the user is associated with the one or        more measurements, the one or more characteristics, or some        combination thereof.

Clause 16.4 The computer-readable medium of any clause herein, whereinthe one or more content items pertain to cardiac rehabilitation,oncology rehabilitation, rehabilitation from pathologies related to theprostate gland or urogenital tract, pulmonary rehabilitation, bariatricrehabilitation, or some combination thereof.

Clause 17.4 The computer-readable medium of any clause herein, whereinthe electromechanical machine is at least one of a cycling machine, arowing machine, and a stair-climbing machine, a treadmill, an andelliptical machine.

Clause 18.4 The computer-readable medium of any clause herein, whereinthe processing devices is to modify one or more risk factors of the userby presenting the one or more content items.

Clause 19.4 The computer-readable medium of any clause herein, whereinthe processing device is to:

-   -   receive, from one or more peripheral devices, input from the        user, wherein the input from the user comprises a request to        view more details related to the information, a request to        receive different information, a request to receive related or        complementary information, a request to stop presentation of the        information, or some combination thereof.

Clause 20.4 The computer-readable medium of any clause herein, wherein,based on the one or more content items, the processing device is tomodify one or more operating parameters of the electromechanicalmachine.

System and Method for Using AI/ML and Telemedicine to Perform BariatricRehabilitation Via an Electromechanical Machine

FIG. 20A generally illustrates an example embodiment of a method 2000for using artificial intelligence and machine learning and telemedicineto perform bariatric rehabilitation via an electromechanical machineaccording to the principles of the present disclosure. Althoughdescribed with respect to bariatric rehabilitation (i.e., rehabilitationperformed subsequent to a bariatric procedure), the principles discussedbelow may also be applied to prehabilitation (i.e., prehabilitationperformed prior to a bariatric procedure). For simplicity, as describedbelow with respect to the present embodiment, the term “rehabilitation”may, in some contexts, refer to both rehabilitation and prehabilitation.

The method 2000 may be performed by processing logic that may includehardware (circuitry, dedicated logic, etc.), software, or a combinationof both. The method 2000 and/or each of the individual functions,subroutines, methods (as the term is used in object-orientedprogramming), or operations may be performed by one or more processingdevices of a computing device (e.g., the system of FIG. 1 , the computersystem 1100 of FIG. 11 ) implementing the method 2000. For example, asingle processing device may be configured to perform all of thefunctions of the method 2000, two or more processors may be configuredto perform respective functions of the method 2000, etc. The method 2000may be implemented as computer instructions stored on a memory deviceand executable by the one or more processing devices. In certainimplementations, the method 2000 may be performed by a single processingthread. Alternatively, the method 2000 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the method.Accordingly, as used herein, “a processing device configured to” or “aprocessor configured to” can be interpreted as a single processingdevice or processor configured to perform all of the recited functionsor as two or more processing devices or processors collectivelyconfigured to perform all of the recited functions. Similarly,“circuitry” or “processing circuitry” can be interpreted as circuitry ofone or more processors, processing devices, or other electronic circuitsconfigured to respectively or collectively perform the recitedfunctions.

In some embodiments, a system may be used to implement the method 2000.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 2000.

At block 2002, the processing device may receive, at a computing device,a first treatment plan designed to treat a bariatric health issue of auser. The first treatment plan may include one or more exercises,exercise sessions, etc. that, based on the bariatric health issue of theuser, enable the user to perform an exercise at different exertionlevels. In some examples, the first treatment plan may be directed tomaximizing and optimizing improvement of bariatric and/or overall healthof the user while minimizing discomfort, pain, etc. related to sideeffects and complications subsequent to a bariatric procedure. In otherexamples, the first treatment plan may be directed to preparing the userfor an upcoming (i.e., future) bariatric procedure, increasing alikelihood that a user will be eligible for the upcoming bariatricprocedure, increasing a likelihood of success of the upcoming bariatricprocedure, reducing a likelihood of complications or side effects fromthe upcoming bariatric procedure, etc.

In some embodiments, the first treatment plan may be generated based onattribute data including, but not limited to, an eating or drinkingschedule of the user, information pertaining to an age of the user,information pertaining to a sex of the user, information pertaining to aweight of the user, information pertaining to a gender of the user, anindication of a mental state of the user, information pertaining to agenetic condition of the user, information pertaining to a disease stateof the user, information pertaining to a microbiome from one or morelocations on or in the user, an indication of an energy level of theuser, information pertaining to a weight of the user, informationpertaining to a height of the user, information pertaining to a bodymass index (BMI) of the user, information pertaining to a family historyof cardiovascular health issues of the user, information pertaining tocomorbidities of the user, information pertaining to desired healthoutcomes of the user if the treatment plan is followed, informationpertaining to predicted health outcomes of the user if the treatmentplan is not followed, or some combination thereof.

The attribute data may further include information associated with theuse of the one or more electromechanical machines to perform one or moretreatment plans, such as which treatment plans the user performed in agiven time period, a frequency and a duration that the user performedeach of the treatment plans, measurement information (and any changes,such as improvements, in the measurement information over time as theuser performed the treatment plans) received while the user performedeach of the treatment plans, or some combination thereof. For exampleonly, the performance information may be indicative of whetherperforming the treatment plans has resulted in any improvement (orworsening) of a bariatric or other condition of the user.

The personal information may include characteristics such as: vital-signor other measurements; performance; demographic; psychographic;geographic; diagnostic; measurement- or test-based; medically historic;behavioral historic; cognitive; etiologic; cohort-associative;differentially diagnostic; surgical, physically therapeutic, microbiomerelated, pharmacologic and other treatment(s) recommended; arterialblood gas and/or oxygenation levels or percentages; glucose levels;blood oxygen levels; insulin levels; psychographics; etc.

The attribute data may include measurement information measured before,while, or after the user performs the treatment plan, such as a vitalsign, a respiration rate, a heartrate, a temperature, a blood pressure,a glucose level, arterial blood gas and/or oxygenation levels orpercentages, or other biomarker, or some combination thereof. Receivedcardiovascular data may include a cardiac output of the user, aheartrate of the user, a heart rhythm of the user, a blood pressure ofthe user, a blood oxygen level of the user, a cardiovascular diagnosisof the user, a non-cardiovascular diagnosis of the user, a respirationrate of the user, spirometry data related to the user, or somecombination thereof. Received pulmonary data may include a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a respiration rate of theuser, a pulmonary diagnosis of the user, an oncologic diagnosis of theuser, a pulmonary diagnosis of the user, a pathological diagnosisrelated to a prostate gland or urogenital tract of the user, spirometrydata related to the user, or some combination thereof.

The attribute data may be received from various data sources, including,but not limited to, the treatment apparatus 70 or any component of thesystems described herein, sensors, an electronic medical record system,an application programming interface, a third-party application, or somecombination thereof.

At block 2004, while the user uses an electromechanical machine toperform the first treatment plan for the user, the processing device mayreceive bariatric data (including any relevant attribute data asdescribed above) from one or more sensors configured to measure thebariatric data associated with the user. In some embodiments, thebariatric data may include a weight of the user, a cardiac output of theuser, a heartrate of the user, a heart rhythm of the user, a bloodpressure of the user, a blood oxygen level of the user, a cardiovasculardiagnosis of the user, a non-cardiovascular diagnosis of the user, arespiration rate of the user, a pulmonary diagnosis of the user, anoncologic diagnosis of the user, a bariatric diagnosis of the user, apathological diagnosis related to a prostate gland or urogenital tractof the user, spirometry data related to the user, or some combinationthereof.

At block 2006, the processing device may transmit the bariatric data. Insome embodiments, one or more machine learning models 13 may be executedby the server 30 and the machine learning models 13 may be used togenerate a second treatment plan based on the bariatric data and/orother attribute data. As used herein, “second treatment plan” may referto either a new treatment plan, adjustments/modifications to the firsttreatment plan, the first treatment plan as modified in accordance withthe machine learning models 13, etc. The second treatment plan maymodify at least one exercise, exertion level, etc., and the modificationmay be based on a standardized measure including perceived exertion,bariatric data, and the bariatric health issue of the user. In someembodiments, the standardized measure of perceived exertion may includea metabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE). The standardized measure may be further based on sensedor measured information, user inputs (e.g., feedback), clinician inputs,or some combination thereof.

In some embodiments, the one or more machine learning models generatethe second treatment plan by predicting exercises that will result in adesired exertion level for each session (e.g., while also minimizingdiscomfort, pain, exacerbation of side effects and complications,minimizing a likelihood that the user will discontinue the treatmentplan, minimizing conflict with comorbid conditions, etc. as describedbelow in more detail). The one or more machine learning models may betrained using data pertaining to the standardized measure of perceivedexertion, other users' bariatric data, and other users' bariatric healthissues as input data, and other users' exertion levels that led todesired results as output data. The input data and the output data maybe labeled and mapped accordingly.

At block 2008, the processing device may receive the second treatmentplan from the server 30. The processing device may implement at least aportion of the treatment plan to cause an operating parameter of theelectromechanical machine to be modified in accordance with the modifiedexertion level set in the second treatment plan. To that end, in someembodiments, the second treatment plan may include a modified parameterpertaining to the electromechanical machine. The modified parameter mayinclude a resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof. The processing device may, based on the modified parameter,control the electromechanical machine.

In some embodiments, transmitting the bariatric data may includetransmitting the bariatric data to a second computing device that relaysthe bariatric data to a third computing device that is associated with ahealthcare professional.

FIG. 20B shows a simplified block diagram of the computer-implementedsystem 10 of FIG. 1 , configured to implement the method 2000 of FIG.20A, a method 2040 of FIG. 20C, etc. Implementing the methods 2000/2040may include using an artificial intelligence and/or machine learningengine to, for bariatric rehabilitation and/or prehabilitation, generateone or more treatment plans, recommend treatment plans, and/or provideexcluded treatment plans that should not be recommended to a patient,adjust treatment plans, etc. as described below in more detail.

The system 10 includes the server 30 configured to store and providedata associated with generating and managing the treatment plan. Theserver 30 may include one or more computers and may take the form of adistributed and/or virtualized computer or computers. The server 30communicates with one or more clinician interfaces 20 (e.g., via thefirst network 34, not shown in FIG. 20B). Although not shown in FIG.20B, the server 30 may further communicate with the supervisoryinterface 90, the reporting interface 92, the assistant interface 94,etc. (referred to collectively, along with the clinician interface 20,as clinician-side interfaces). The processor 36, memory 38, and the AIengine 11 (e.g., implementing the machine learning models 13) areconfigured to implement the methods 2000/2040.

For example, the attribute data (e.g., bariatric and other attributedata defined above) may be stored in the memory 38 (e.g., along with theother data stored in the data store 44 as described above in FIG. 1 ).The attribute data may be received via the clinician interface 20 and/orother clinician-side interfaces, the patient interface 50 and/or thetreatment apparatus 70 (e.g., via the second network 58), directly fromvarious sensors, etc.

The stored attribute data is accessible by the processor 36 to enablegeneration and/or modification of at least one treatment plan for theuser in accordance with the attribute data. For example, in someembodiments, the processor 36 is configured to execute instructionsstored in the memory 38 and to implement the AI engine 11 to generatethe treatment plan, wherein the treatment plan includes one or moreexercises directed to improving (or increasing a probability ofimproving) bariatric or overall health subsequent to a bariatricprocedure, preparing the user for an upcoming bariatric procedure,improving eligibility of the user for an upcoming bariatric procedure,minimizing pain, discomfort, side-effects, complications, etc.associated with having undergone a bariatric procedure, minimizinginterference with comorbid conditions, and so on. The treatment plan mayspecify parameters including, but not limited to, which exercises toinclude or omit, intensities of various exercises, limits (e.g., minimumheartrates, maximum heartrates, minimum and maximum exercise speeds(e.g., pedaling rates), minimum and maximum forces or intensitiesexerted by the user, etc.), respective durations and/or frequencies ofthe exercises, adjustments to make to the exercises while the treatmentapparatus is being used to implement the treatment plan, etc.Adjustments to the treatment plan can be performed, as described belowin more detail, at the server 30 (e.g., using the processor 36, the AIengine 11, etc.), the clinician-side interfaces, and/or the treatmentapparatus 70.

The server 30 provides the treatment plan to the treatment apparatus 70(e.g., via the second network 58, the patient interface 50, etc.). Thetreatment apparatus 70 is configured to implement the one or moreexercises of the treatment plan. For example, the treatment apparatus 70may be responsive to commands supplied by the patient interface 50and/or a controller of the treatment apparatus 70 (e.g., the controller72 of FIG. 1 ). In one example, the processor 60 of the patientinterface 50 is configured to execute instructions (e.g., instructionsassociated with the treatment plan stored in the memory 62) to cause thetreatment apparatus 70 to implement the treatment plan. In someexamples, based on information associated with the user and real-timedata (e.g., measurement information, such as sensor or other datareceived while the user is performing the one or more exercises usingthe treatment apparatus), user inputs, etc., the patient interface 50and/or the treatment apparatus 70 may be configured to adjust thetreatment plan and/or individual exercises.

In order to generate the treatment plan, the server 30 according to thepresent disclosure may be configured to execute, using the AI engine 11,one or more ML models 13. For example, the ML models 13 may include, butare not limited to, an attribute data model (or models) 13-1, aprobability model (or models) 13-2, and a treatment plan model (ormodels) 13-3, referred to collectively as the ML models 13. Each of theML models 13 may include different layers of nodes as described above.

Although shown as separate models, features of each of the ML models 13may be implemented in a single model or type of model, such as thetreatment plan model 13-3. For example, the treatment plan model 13-3may be configured to receive, as input, the attribute data, determinebariatric or overall health of a user based on the attribute data,determine eligibility of the user for a bariatric procedure, determinevarious probabilities associated with the attribute data, and generate atreatment plan (e.g., to increase or decrease respective probabilities)in accordance with the principles of the present disclosure.

The attribute data model 13-1 is configured to receive the attributedata (including bariatric data) and related inputs and, in someexamples, to exclude and add attribute data (e.g., apply filtering tothe attribute data), generate relative weights for the attribute data,and update the attribute data based on external inputs (e.g., receivedfrom the clinician-side interfaces and/or the patient interface 50),etc. The attribute data model 13-1 is configured to output, to theprobability model 13-2, a selected set of the attribute data (referredto herein as “selected attribute data”), which may include weighted ormodified attribute data. In some examples, the attribute data model 13-1may be omitted and the attribute data may be provided directly to theprobability model 13-2 and/or the treatment plan model 13-3.

The probability model 13-2 is configured to determine, based on theselected attribute data received from the attribute data model 13-1,eligibility of the user for a bariatric procedure (e.g., a probabilitythat the user will be considered eligible for the bariatric procedure)and various other probabilities associated with the bariatric andoverall health of the user. For example, the various probabilitiesinclude, but are not limited to, a probability that the bariatric oroverall health of the user will improve or worsen (e.g., with or withoutundergoing the bariatric procedure, with or without performing atreatment plan, etc.), a probability that a desired goal of the user(e.g., a target weight or weight loss, the ability to perform a specificphysical activity, etc.) will be attained (e.g., with or withoutundergoing the bariatric procedure, with or without performing atreatment plan, etc.), a probability of side effects or complicationssubsequent to the bariatric procedure, probabilities that one or morecomorbid conditions will improve or worsen, and so on. Each probabilitymay be dependent upon the selected attribute data and any assignedweights, usage history of the treatment apparatus 70 by the user, cohortdata (as described above), and/or environmental and other external orvariable data (e.g., current air conditions, temperature, climate,season or time of year, time of day, etc.).

The treatment plan model 13-3 is configured to generate the treatmentplan directed to change one or more of the various probabilities (e.g.,increase probabilities of favorable outcomes/results or decrease aprobabilities of unfavorable results). In some embodiments, thetreatment plan model 13-3 is configured to: modify the treatment plan toincrease the probability that the user will be considered eligible forthe bariatric procedure; modify the treatment plan to increase theprobability that that the bariatric or overall health of the user willimprove or worsen; modify the treatment plan to increase the probabilitythat a desired goal of the user will be attained; modify the treatmentplan to decrease the probability that side effects, complications, painor discomfort, etc. associated with the bariatric procedure will occur;modify the treatment plan to decrease the probability that one or morecomorbid conditions of the user will be exacerbated/aggravated; andcombinations thereof.

To increase (or decrease) the various probabilities described above, thetreatment plan may include one or more exercises associated withimproving specific health conditions or characteristics of the usercontributing to the bariatric or overall health of the user, such asimproving one or more of: bariatric conditions of the user; bloodpressure, blood vessel characteristics, blood oxygen levels, and/or anyother cardiac- or cardiovascular-related condition of the user;pulmonary conditions of the user; oncological conditions of the user;orthopedic conditions of the user; weight or BMI of the user; overallphysical activity levels of the user; and/or any combination of anyspecific condition of the user described herein. For example, if weightor BMI is determined to be a highest contributor to a probability thatbariatric health will improve, the treatment plan may be configuredspecifically to reduce the BMI of the user. As another example, if highblood pressure is the highest contributor to a low probability that theuser will be eligible for the bariatric procedure, the treatment planmay be configured specifically to reduce the blood pressure of the user.As still another example, if high blood pressure and risk ofside-effects are each determined to be highest contributors (e.g., equalor near equal contributors) to a low probability that the user will beeligible for the bariatric procedure, the treatment plan may beconfigured (e.g., balanced) to reduce both the blood pressure of theuser and the risk of side effects.

The treatment plan may include, but is not limited to, specific targetexercises for the user to perform using the treatment apparatus 70,suggested replacement or alternative exercises (e.g., in the event thatthe user is unable to perform one or more of the target exercises due todiscomfort), parameters/limits for each of the exercises (e.g.,duration, intensity, repetitions, etc.), and excluded exercises (e.g.,exercises that should not be performed by the user). In some examples,rather than including only specific exercises, the treatment plan mayinclude one or more exercise parameters (e.g., resistance or force,intensity, range of motion, etc.) or user conditions/measurements (e.g.,heartrates, breathing rates or respiratory behavior, METcharacteristics, specific movements, weight loss, etc.) associated withimproving a health condition. For example, the treatment plan mayspecify one or more desired ranges of values for various characteristics(e.g., a heartrate range).

The treatment plan model 13-3 may be further configured to generate,based on the various probabilities, a recommendation regarding whetherthe user should undergo the bariatric procedure. In some examples, therecommendation may be binary (e.g., a value indicating “yes” or “no”).In other examples, the recommendation may include a score or rankedvalue (e.g., a score between 1 and 100, where “1” is a minimumrecommendation and a “100” is a maximum recommendation). In still otherexamples, the recommendation may include various recommendation tiers(which may be based on a corresponding score between 1 and 100), such as“strongly recommend against,” “recommend against,” “recommend for,” and“strongly recommend for.” The recommendation may be provided to the uservia the patient interface 50 of the treatment apparatus, the clinicianinterface 20, etc.

In some examples, the recommendation may be based on simple, directcomparison between the probability that the bariatric procedure willimprove the bariatric or overall health of the user and a probabilitythreshold. In other examples, as described above, the recommendation maybe based on a more complex analysis including: the comparison betweenthe probability and the probability threshold; a probability that thebariatric or overall health of the user will improve without theundergoing the bariatric procedure; a probability that the bariatricprocedure will result in worsening of comorbid conditions; orcombinations thereof.

In still other examples, the recommendation may include information thatindicates progress toward one or more goals related to satisfyingeligibility requirements for the bariatric procedure. For example, arecommendation against undergoing the one or more procedures may bebased on specific components of the attribute data being below acorresponding threshold. Accordingly, the recommendation may include agoal for the specific components in order to qualify for the bariatricprocedure, a progress (e.g., as a percentage or other indicator)) towardthe goal, etc. As one example, a recommendation against the bariatricprocedure may be based on a weight or BMI being greater than athreshold. The recommendation may include a goal for the weight or BMI(e.g., below a threshold value) in order to qualify for the bariatricprocedure, as well as a progress toward that goal (e.g., 50% of theoverall weight reduction achieved). The recommendation may includemodifications to the treatment plan based on the one or more goalsand/or progress toward the one or more goals.

FIG. 20C illustrates an example method 2020 for generating a treatmentplan for bariatric rehabilitation and/or prehabilitation according tothe present disclosure. The method 2020 expands upon the method 2000described above in FIG. 20A with additional details described above inFIG. 20B. The system 10 described in FIG. 20B may be configured toperform the method 2020. As described herein, generating the treatmentplan for bariatric rehabilitation and/or prehabilitation may includegenerating the treatment plan to increase or decrease variousprobabilities associated with eligibility of the user for the bariatricprocedure, prehabilitation of the user in preparation for the bariatricprocedure, rehabilitation subsequent to the bariatric procedure, etc.While described below with respect to increasing or decreasing thevarious probabilities, in some examples systems and methods may beconfigured to generate the treatment plan to more generally improvebariatric or overall health without calculating and/or being responsiveto specific probabilities.

At 2022, the system 10 (e.g., the attribute data model 13-1) receivesthe attribute data, which includes bariatric data and other datadescribed above. The attribute data may include both non-modifiable andstatic characteristics associated with the user and modifiable ordynamic characteristics associated with the user. Non-modifiablecharacteristics may include, but are not limited to, genetic factors,family history, age, sex, cardiac history, comorbidities, diabetichistory, oncological history (e.g., whether the user has previouslyundergone chemotherapy and/or radiation treatment), etc. Modifiablecharacteristics may include, but are not limited to, heartrate, bloodpressure, current diabetic status, blood oxygen (SpO2) levels,cholesterol, weight, diet, lipid levels in the blood, tobacco use,alcohol use, current medications, blood pressure, physical activitylevel, psychological factors (e.g., depression or anxiety), etc.

The attribute data may include performance information. The performanceinformation may include, inter alia, information associated with the useof the one or more electromechanical machines to perform one or moretreatment plans. In other words, the method 2020 may be implementedwhile the user has already been performing a previously generatedtreatment plan (e.g., for a period of weeks, months, etc.). In someexamples, the treatment plan may correspond to a treatment planprescribed to the user in preparation for the bariatric procedure or atreatment plan prescribed to the user for rehabilitation subsequent tothe bariatric procedure. Accordingly, in some examples, the method 2020may correspond to an assessment of progress of the user toward a goal ofqualifying for the bariatric procedure and a recommendation of whetherto undergo the bariatric procedure.

At 2024, the system 10 (e.g., the attribute data model 13-1, as executedby the AI engine 11, the processor 36, etc.) generates and outputs aselected set of attribute data. In some examples, the selected set ofattribute data comprises of all received attribute data. In otherexamples, the attribute data model 13-1 applies filtering to theattribute data (e.g., to exclude certain user characteristics that maynot contribute to the various probabilities calculated by the method2020), or applies weights to or ranks (e.g., assigns a priority valueto) components of the attribute data, etc. As one example, somecomponents of the attribute data may have a greater correlation withbariatric rehabilitation or prehabilitation. Conversely, othercomponents of the attribute data may have a lesser correlation withbariatric rehabilitation or prehabilitation. Some components of theattribute data may be binary (i.e., simply present or not present, suchas diabetic history) and may be assigned a binary weight such as 0 or 1while other components of the attribute data may have a variablecontribution to the various probabilities, such as blood pressure, andmay be assigned a decimal value between 0 and 1. Components of theattribute data that are determined to have a stronger than averagecorrelation with bariatric rehabilitation or prehabilitation may beassigned a weight greater than 1 (1.1, 1.5, 2.0, etc.). In someexamples, the attribute data model 13-1 may be configured to categorizecomorbid conditions indicated in the attribute data as described belowin more detail.

At 2026, the system 10 (e.g., the probability model 13-2, as executed bythe AI engine 11, the processor 36, etc.) receives the set of attributedata and, based on the selected attribute data and associated weightsand/or ranking, calculates eligibility of the user for a bariatricprocedure and various other probabilities associated with the bariatricand overall health of the user as described above, such as a probabilitythat the bariatric or overall health of the user will improve or worsen,a probability that a desired goal of the user associated with bariatrichealth will be attained, a probability of side effects or complicationssubsequent to the bariatric procedure, probabilities that one or morecomorbid conditions will improve or worsen, and so on. In some examples,the probability model 13-2 may further adjust (e.g., increase ordecrease, exclude, etc.), based on additional data, such asenvironmental data or other variable data as described, any of theattribute data. For example, some characteristics contained in theattribute data may be exacerbated by conditions such as air conditionsin a geographic region associated with the user, climate, etc. Thisadjustment of the attribute data may also be performed using theattribute data model 13-1.

The probability model 13-2 calculates each of the various probabilitiesas a probability value or values, a confidence interval, anon-probabilistic value, a numerical value, etc. As one example, theprobability values may correspond to Bayesian probabilities, Markovianprobabilities, a stochastic prediction, a deterministic prediction, etc.Each of the probability values may be calculated based on a combinationof components of the attribute data and respective weights/valuesprovided by the attribute data model 13-1. For example, a probabilityvalue may be calculated based on a respective probability of improving aspecific bariatric or other condition associated with each component ofthe attribute data. In other words, each component of the attribute datamay have an associated probability or contribution to a givenprobability. By using all of the probability values of the attributedata in the received set of attribute data, the probability model 13-2may calculate an overall probability associated with the bariatrichealth of the user. In one example, the respective probabilities of eachof the components of the attribute data may be weighted.

At 2028, in some embodiments, the system 10 (e.g., the treatment planmodel 13-3, as executed by the AI engine 11, the processor 36, etc.)receives the probabilities determined at 2026 and, based on theprobabilities, generates one or more recommendations regarding whetherthe user should undergo a bariatric procedure (e.g., recommendations foror against undergoing the bariatric procedure). The recommendations mayinclude one or more of: a binary “yes” or “no” recommendation; a scoreor ranked recommendation value; multiple scores or recommendations forthe bariatric procedure; information that indicates progress toward oneor more goals related to qualifying for the bariatric procedure;modifications to the treatment plan based on the one or more goalsand/or progress toward the one or more goals; etc.

In some examples, the recommendation is generated based only on acomparison between a probability of the bariatric procedure improvingthe bariatric or overall health of the user and a probability threshold.In other examples, the recommendation may be based on both thecomparison between the probability and the probability threshold and acomparison between the probability and a probability that the bariatricor overall health of the user will improve without undergoing thebariatric procedure.

At 2030, the method 2020 (e.g., the treatment plan model 13-3, asexecuted by the AI engine 11, the processor 36, etc.) receives theprobabilities calculated by the probability model 13-2 and modifies thetreatment plan accordingly (i.e., based on the probabilities and, insome examples, the recommendations generated at 2028). For example, inresponse to a recommendation that the user undergoes the bariatricprocedure, the treatment plan model 13-3 may modify the treatment planto prepare the user for the bariatric procedure, to prepare the user forrehabilitation subsequent to the bariatric procedure, etc. Conversely,in response to a recommendation that the user does not undergo thebariatric procedure, the treatment plan model 13-3 may modify thetreatment plan to improve the eligibility of the user for the bariatricprocedure (e.g., improve a probability/likelihood that the user willqualify for the bariatric procedure in a subsequent assessment), toimprove the bariatric or overall health of the user without undergoingthe bariatric procedure, etc. In some examples, the treatment plan model13-3 receives, in addition to the calculated probabilities andrecommendations, the attribute data.

To improve eligibility of the user for the bariatric procedure (e.g.,increase the probability that the user will qualify for the bariatricprocedure in a subsequent assessment), the treatment plan may includeone or more exercises or exercise routines associated with improving oneor more bariatric or other health conditions of the user associated witheligibility. The treatment plan may include, but is not limited to,specific target exercises for the user to perform using the treatmentapparatus 70, suggested replacement or alternative exercises,parameters/limits for each of the exercises, and/or excluded exercises.

As one example, the treatment plan model 13-3 may generate the treatmentplan in accordance with generalized parameters associated with improvingone or more specific health conditions associated with eligibility forthe bariatric procedure. For example, for a specific one or more of thetreatment apparatuses 70 being used with the system 10, the treatmentplan may specify parameters including, but not limited to, one or moreexercises (e.g., in systems where the treatment apparatus 70 isconfigured to implement more than one exercise, in systems with multipletreatment apparatuses, etc.) to perform, a frequency of each exercise, aduration of each exercise, settings for the treatment apparatus 70during the exercise (e.g., resistance, intensity, speed, slope, etc.),and desired ranges for various measured, sensed, and/or calculatedcharacteristics of the user while the user performs the treatment plan(e.g., heartrate).

In still other examples, parameters of the treatment plan or one or moreexercises may be limited or, based on specific attribute data, exercisesmay be excluded. For example, the presence of one or more components ofthe attribute data may increase the probability of discomfort,discontinued use, injury to the user, etc. Accordingly, the treatmentplan may limit parameters such as intensity, frequency, duration, etc.

In still another example, the system 10 may be configured to generate atreatment plan configured to manage other health conditions, riskfactors, etc. The system 10 may be further configured to adjust thetreatment plan to improve the eligibility while also targeting the otherhealth conditions. For example, the treatment plan may include one ormore exercises, parameters, etc. directed to improving a first healthcondition. The system 10 may add or omit exercises, extend or limitdesired ranges of operating parameters and/or measured usercharacteristics, etc. to improve the probability of being eligible forthe bariatric procedure, all while still targeting the first healthcondition.

Similarly, for prehabilitation prior to the bariatric procedure (i.e.,to prepare the user for the bariatric procedure), the treatment plan mayinclude one or more exercises or exercise routines associated withimproving one or more bariatric or other health conditions of the userassociated with a desired outcome of the bariatric procedure. As oneexample, the treatment plan model 13-3 may generate the treatment planin accordance with generalized parameters associated with improving oneor more specific bariatric or other health conditions associated with anoutcome of the bariatric procedure. In still other examples, parametersof the treatment plan or one or more exercises may be limited or, basedon specific attribute data, exercises may be excluded. In still anotherexample, the system 10 may be configured to generate a treatment planconfigured to manage other health conditions, risk factors, etc. whilepreparing the user for the bariatric procedure.

To improve bariatric or overall health subsequent to the bariatricprocedure (i.e., for rehabilitation) or to improve bariatric or overallhealth for a user that did not undergo the bariatric procedure, thetreatment plan may include one or more exercises or exercise routinesassociated with improving one or more bariatric or other healthconditions of the user (e.g., increasing a probability that thebariatric or overall health of the user will improve). As one example,the treatment plan model 13-3 may generate the treatment plan inaccordance with generalized parameters associated with improving one ormore specific bariatric or other health conditions. In still otherexamples, parameters of the treatment plan or one or more exercises maybe limited or, based on specific attribute data, exercises may beexcluded. In still another example, the system 10 may be configured togenerate a treatment plan configured to manage other health conditions,risk factors, etc. while performing rehabilitation subsequent to thebariatric procedure.

In some examples, the treatment plan may be configured to increase aprobability that a desired goal of the user associated with bariatrichealth will be attained (e.g., prior to and/or subsequent to thebariatric procedure, in lieu of undergoing the bariatric procedure,etc.). For example, the attribute data may include an indication of oneor more goals of the user as input by the user, a clinician, etc. Thegoals may correspond to specific physical activities that may be limitedby one or more bariatric conditions (e.g., swimming, walking apredetermined distance, participating in a specific sport orrecreational activity (e.g., watching a movie, play, etc. in a theater),travel (e.g., air travel), etc.), and/or the goals may correspond tospecific physical attributes (e.g., a target weight, BMI, bloodpressure, etc.). The treatment plan model 13-3 may generate thetreatment plan in accordance with generalized parameters associated withattaining the one or more goals. Parameters of the treatment plan or oneor more exercises may be limited or, based on specific attribute data,exercises may be excluded. The system 10 may be configured to generate atreatment plan configured to manage other health conditions, riskfactors, etc. while increasing the probability that the user will attainthe one or more goals.

In any of the above examples, the treatment plan may be furtherconfigured to perform rehabilitation and/or prehabilitation (e.g.,increase probabilities of desirable outcomes associated with improvedbariatric or overall health) while decreasing a probability of sideeffects or complications subsequent to the bariatric procedure,increasing (or decreasing) probabilities that one or more comorbidconditions will improve (or worsen), and so on. For example, users thatare candidates for and/or have undergone a bariatric procedure typicallyhave one or more comorbid conditions (e.g., comorbid conditions causedby or otherwise correlated with obesity, such as diabetes, high bloodpressure, etc., and/or comorbid conditions caused by the bariatricprocedure, such as pain or discomfort following surgery, nutritionaldeficiencies, etc.). Side effects or complications subsequent to thebariatric procedure may include, but are not limited to: short term sideeffects such as infections; bleeding; blood clots; short-term digestiveissues (e.g., nausea); etc. and long term side effects such asnutritional deficiencies; long-term digestive issues (e.g., dumpingsyndrome); hernia; bowel obstruction; ulcers; pancreatitis; gallstones;anemia; osteoporosis; psychological issues such as depression andanxiety, etc.

These and other comorbid conditions (which, as used herein forsimplicity, may refer to side effects or complications associated withthe bariatric procedure as described above) may interfere with bariatricrehabilitation and prehabilitation. For example: pain, discomfort,physical limitations, etc. associated with comorbid conditions may limitthe ability of the user to perform the treatment plan, cause the user toavoid performing the treatment plan, etc.; performing the treatment planmay worsen or cause (e.g., increase the risk of occurrence of) comorbidconditions; comorbid conditions may decrease the effectiveness of thetreatment plan; etc. Accordingly, generating (and implementing) thetreatment plans according to the present disclosure includes managing,compensating for, and preventing complications caused by comorbidconditions during bariatric rehabilitation and prehabilitation.

For example, absent comorbid conditions, risks of side effects orcomplications, etc., the treatment plan may be configured to simplymaximize effectiveness of various exercises for rehabilitation orprehabilitation purposes (e.g., by maintaining heartrate, intensity,etc.). However, the systems and methods of the present disclosure areconfigured to generate the treatment plan in accordance with comorbidconditions indicated by the attribute data as described above.

As one example, the comorbid conditions may be categorized (e.g., at2024, by the attribute data model 13-1, another one of the models 13,etc.). Categorizing the comorbid conditions may include assigning acategory value or identifier to each comorbid condition based on anassigned category, ranking or assigning weights to comorbid conditions,etc. As a simplified example, comorbid conditions may be assigned to oneof three categories: a first category (e.g., a category value of 0)indicating that the comorbid condition has no effect on rehabilitationor prehabilitation and should not be considered; a second category(e.g., a category value of 1) indicating that the comorbid condition isassociated with pain or discomfort that may be experienced by the userwhile performing the treatment plan; and a third category (e.g., acategory value of 2) indicating that the comorbid condition isassociated with a potential health or safety risk while performing thetreatment plan (e.g., a heartrate, oxygen level, etc. being outside of adesired range). Within each category, each comorbid condition may alsobe assigned a relative severity (e.g., a value between 0 and 1, a valuefrom 1 to 5, etc.) and/or an associated parameter limit. For example,the parameter limit may indicate a range or value of an exerciseparameter (e.g., rate or pace, range of motion, force or intensity)and/or a sensed or measured characteristic of the user (heartrate,oxygen level, etc.) that is not to be exceeded.

Accordingly, each comorbid condition may have an associated categoryvalue, severity value, and/or parameter limit. In this manner, themethod 2020 is configured to generate the treatment plan further basedon comorbid conditions (and/or existing or potential side effects orcomplications) of the user arising from, predating, and/or otherwiseassociated with the bariatric procedure and bariatric or overall health.

For example, rather than simply generating the treatment plan tomaximize effectiveness of various exercises for rehabilitation orprehabilitation purposes, the method 2020, based on comorbid conditionsindicated by the attribute data: modifies or limits parameters (e.g., ofexercises, the treatment apparatus 70, etc.) and/or sensed or measuredcharacteristics of the user, etc.; adds, omits, and/or substitutesexercises; changes durations or frequencies of individual exercises orsessions; generates alerts or warnings for the user and/or clinician;generates recommendations for the user or clinician for modifying thetreatment plan to accommodate one or more comorbid conditions; and/orcombinations thereof.

The method 2020 may further generate or modify the treatment plan basedon cohort data (e.g., attribute data, treatment plan results, etc.associated with other users in a same cohort as the user that have alsohad and/or prepared for a bariatric procedure). For example, as users ina cohort perform bariatric rehabilitation or prehabilitation over time,the models 13 may be updated in view of comorbid conditions that arecaused by, worsened, etc. as a result of various exercises or othertreatment plan parameters, in view of comorbid conditions that improvedas a result of various exercises or other treatment plan parameters,etc. Accordingly, rather than modifying the treatment plan simply basedon the attribute data and treatment plan results of the user, the method2020 generates/modifies the treatment plan further based on the cohortdata. For example, thresholds or ranges for parameters, sensedcharacteristics, etc. may be further limited (i.e., made morerestrictive) in view of cohort data indicating that certain comorbidconditions were likely to worsen, result in pain or discomfort, etc.while users performed the treatment plan. Conversely, thresholds orranges for parameters, sensed characteristics, etc. may be increased(i.e., made less restrictive) in view of cohort data indicating thatcertain comorbid conditions were not likely to worsen, result in pain ordiscomfort, etc. while users performed the treatment plan.

At 2032, the system 10 (e.g., the patient interface 50 and/or thetreatment apparatus 70) implements the one or more exercises of thetreatment plan. For example, the treatment plan is transmitted to thepatient interface 50 to enable the treatment apparatus 70 to implementthe one or more exercises, the user initiates the one or more exercisesusing the patient interface 50, etc. In some examples, based onreal-time data, the system 10 (e.g., the patient interface 50 and/or thetreatment apparatus 70) optionally adjusts the one or more exercisesbeing implemented. For example, the treatment apparatus 70, the patientinterface 50, and/or other components of the system 10 receive, from oneor more sensors, one or more measurements associated with the user. Theone or more measurements may be received while the user performs thetreatment plan. Example adjustments include, but are not limited to,increasing or decreasing intensity or other parameters to increase ordecrease heartrate, metabolic equivalent of task (MET, a ratio ofworking metabolic rate to resting metabolic rate, as defined here andreferenced elsewhere herein), etc. For example, the adjustments may bemade to maintain a heartrate of the user within a target range (i.e.,without decreasing below a lower limit or increasing above an upperlimit) configured to increase or decrease any of the variousprobabilities described above.

At 2034, the system 10 (e.g., the patient interface 50 and/or thetreatment apparatus 70) determines whether a current session of thetreatment plan has been completed. If true, the method 2020 proceeds to2036. If false, the method 2020 proceeds to 2032. At 2036, based on thecompleted treatment plan session, the system 10 (e.g., the server 30,implementing the models 13) updates or modifies the treatment planand/or attribute data. For example, the treatment plan model 13-3 mayadd exercises to or remove exercises from the treatment plan, adjustparameters of exercises, change the frequency or duration of exercises,etc. As one example, the treatment plan model 13-3 may reduceintensities in response to a determination that the heartrate of theuser exceeded the target range, increase intensities in response to adetermination that the heartrate of the user did not reach the targetrange, increase or decrease the target range, etc. In some examples, tolimit maximum heartrate and rate of heartrate increase while stillincreasing (or decreasing) any of the various probabilities describedabove, the treatment plan model 13-3 may be adjusted based on previousimplementations. In some examples, any modification of the treatmentplan must be approved by a healthcare professional (e.g., via theclinician-side interfaces) prior to implementation by the treatmentapparatus 70.

Clauses

Clause 1.5 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user performs a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, at a computing device, a first treatment plan designed        to treat a bariatric health issue of a user, wherein the first        treatment plan comprises at least two exercise sessions that,        based on the bariatric health issue of the user, enable the user        to perform an exercise at different exertion levels;    -   while the user uses a treatment apparatus to perform the first        treatment plan for the user, receive bariatric data from one or        more sensors configured to measure the bariatric data associated        with the user;    -   transmit the bariatric data, wherein one or more machine        learning models are used to generate a second treatment plan,        wherein the second treatment plan modifies at least one exertion        level, and the modification is based on a standardized measure        comprising perceived exertion, the bariatric data, and the        bariatric health issue of the user; and    -   receive the second treatment plan.

Clause 2.5 The computer-implemented system of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof, and the computer-implemented system further comprises:

-   -   based on the modified parameter, controlling the        electromechanical machine.

Clause 3.5 The computer-implemented system of any clause herein, whereinthe standardized measure of perceived exertion comprises a metabolicequivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

Clause 4.5 The computer-implemented system of any clause herein,wherein, by predicting exercises that will result in the desiredexertion level for each session, the one or more machine learning modelsgenerate the second treatment plan, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' bariatric data, and other users'bariatric health issues.

Clause 5.5 The computer-implemented system of any clause herein, whereinthe first treatment plan is generated based on attribute data comprisingan eating or drinking schedule of the user, information pertaining to anage of the user, information pertaining to a sex of the user,information pertaining to a weight of the user information pertaining toa gender of the user, an indication of a mental state of the user,information pertaining to a genetic condition of the user, informationpertaining to a disease state of the user, information pertaining to amicrobiome from one or more locations on or in the user, an indicationof an energy level of the user, information pertaining to a weight ofthe user, information pertaining to a height of the user, informationpertaining to a body mass index (BMI) of the user, informationpertaining to a family history of cardiovascular health issues of theuser, information pertaining to comorbidities of the user, informationpertaining to desired health outcomes of the user if the treatment planis followed, information pertaining to predicted health outcomes of theuser if the treatment plan is not followed, or some combination thereof.

Clause 6.5 The computer-implemented system of any clause herein, whereinthe transmitting the bariatric data further comprises transmitting thebariatric data to a second computing device that relays the bariatricdata to a third computing device that is associated with a healthcareprofessional.

Clause 7.5 The computer-implemented system of any clause herein, whereinthe bariatric data comprises a weight of the user, a cardiac output ofthe user, a heartrate of the user, a heart rhythm of the user, a bloodpressure of the user, a blood oxygen level of the user, a cardiovasculardiagnosis of the user, a non-cardiovascular diagnosis of the user, arespiration rate of the user, a pulmonary diagnosis of the user, anoncologic diagnosis of the user, a bariatric diagnosis of the user, apathological diagnosis related to a prostate gland or urogenital tractof the user, spirometry data related to the user, or some combinationthereof.

Clause 8.5 A computer-implemented method comprising:

-   -   receiving, at a computing device, a first treatment plan        designed to treat a bariatric health issue of a user, wherein        the first treatment plan comprises at least two exercise        sessions that, based on the bariatric health issue of the user,        enable the user to perform an exercise at different exertion        levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receiving bariatric data from        one or more sensors configured to measure the bariatric data        associated with the user, wherein the electromechanical machine        is configured to be manipulated by the user while the user        performs the first treatment plan;    -   transmitting the bariatric data, wherein one or more machine        learning models are used to generate a second treatment plan,        wherein the second treatment plan modifies at least one exertion        level, and the modification is based on a standardized measure        comprising perceived exertion, the bariatric data, and the        bariatric health issue of the user; and    -   receiving the second treatment plan.

Clause 9.5 The computer-implemented method of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof, and the computer-implemented method further comprises:

-   -   based on the modified parameter, controlling the        electromechanical machine.

Clause 10.5 The computer-implemented method of any clause herein,wherein the standardized measure of perceived exertion comprises ametabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE).

Clause 11.5 The computer-implemented method of any clause herein,wherein, by predicting exercises that will result in the desiredexertion level for each session, the one or more machine learning modelsgenerate the second treatment plan, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' bariatric data, and other users'bariatric health issues.

Clause 12.5 The computer-implemented method of any clause herein,wherein the first treatment plan is generated based on attribute datacomprising an eating or drinking schedule of the user, informationpertaining to an age of the user, information pertaining to a sex of theuser, information pertaining to a weight of the user informationpertaining to a gender of the user, an indication of a mental state ofthe user, information pertaining to a genetic condition of the user,information pertaining to a disease state of the user, informationpertaining to a microbiome from one or more locations on or in the user,an indication of an energy level of the user, information pertaining toa weight of the user, information pertaining to a height of the user,information pertaining to a body mass index (BMI) of the user,information pertaining to a family history of cardiovascular healthissues of the user, information pertaining to comorbidities of the user,information pertaining to desired health outcomes of the user if thetreatment plan is followed, information pertaining to predicted healthoutcomes of the user if the treatment plan is not followed, or somecombination thereof.

Clause 13.5 The computer-implemented method of any clause herein,wherein the transmitting the bariatric data further comprisestransmitting the bariatric data to a second computing device that relaysthe bariatric data to a third computing device that is associated with ahealthcare professional.

Clause 14.5 The computer-implemented method of any clause herein,wherein the bariatric data comprises a weight of the user, a cardiacoutput of the user, a heartrate of the user, a heart rhythm of the user,a blood pressure of the user, a blood oxygen level of the user, acardiovascular diagnosis of the user, a non-cardiovascular diagnosis ofthe user, a respiration rate of the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a bariatric diagnosis of theuser, a pathological diagnosis related to a prostate gland or urogenitaltract of the user, spirometry data related to the user, or somecombination thereof.

Clause 15.5 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive a first treatment plan designed to treat a bariatric        health issue of a user, wherein the first treatment plan        comprises at least two exercise sessions that, based on the        bariatric health issue of the user, enable the user to perform        an exercise at different exertion levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive bariatric data from        one or more sensors configured to measure the bariatric data        associated with the user, wherein the electromechanical machine        is configured to be manipulated by the user while the user        performs the first treatment plan;    -   transmit the bariatric data, wherein one or more machine        learning models are used to generate a second treatment plan,        wherein the second treatment plan modifies at least one exertion        level, and the modification is based on a standardized measure        comprising perceived exertion, the bariatric data, and the        bariatric health issue of the user; and    -   receive the second treatment plan.

Clause 16.5 The computer-readable medium of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof, and the computer-implemented method further comprises:

-   -   based on the modified parameter, controlling the        electromechanical machine.

Clause 17.5 The computer-readable medium of any clause herein, whereinthe standardized measure of perceived exertion comprises a metabolicequivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

Clause 18.5 The computer-readable medium of any clause herein, wherein,by predicting exercises that will result in the desired exertion levelfor each session, the one or more machine learning models generate thesecond treatment plan, and the one or more machine learning models aretrained using data pertaining to the standardized measure of perceivedexertion, other users' bariatric data, and other users' bariatric healthissues.

Clause 19.5 The computer-readable medium of any clause herein, whereinthe first treatment plan is generated based on attribute data comprisingan eating or drinking schedule of the user, information pertaining to anage of the user, information pertaining to a sex of the user,information pertaining to a weight of the user information pertaining toa gender of the user, an indication of a mental state of the user,information pertaining to a genetic condition of the user, informationpertaining to a disease state of the user, information pertaining to amicrobiome from one or more locations on or in the user, an indicationof an energy level of the user, information pertaining to a weight ofthe user, information pertaining to a height of the user, informationpertaining to a body mass index (BMI) of the user, informationpertaining to a family history of cardiovascular health issues of theuser, information pertaining to comorbidities of the user, informationpertaining to desired health outcomes of the user if the treatment planis followed, information pertaining to predicted health outcomes of theuser if the treatment plan is not followed, or some combination thereof.

Clause 20.5 The computer-readable medium of any clause herein, whereinthe transmitting the bariatric data further comprises transmitting thebariatric data to a second computing device that relays the bariatricdata to a third computing device that is associated with a healthcareprofessional.

System and Method for Using AI/ML and Telemedicine to Perform PulmonaryRehabilitation Via an Electromechanical Machine

FIG. 21A generally illustrates an example embodiment of a method 2100for using artificial intelligence, machine learning and telemedicine toperform pulmonary rehabilitation via an electromechanical machineaccording to the principles of the present disclosure. Althoughdescribed with respect to pulmonary rehabilitation (e.g., rehabilitationsubsequent to a pulmonary procedure or treatment, or simply subsequentto a diagnosis of a pulmonary condition), the principles discussed belowmay also be applied to prehabilitation (i.e., prehabilitation performedprior to a pulmonary procedure). For simplicity, as described below withrespect to the present embodiment, the term “rehabilitation” may, insome contexts, refer to both or either of rehabilitation andprehabilitation.

As used below with respect to this embodiment, this disclosure may usethe term “pulmonary” to refer to pulmonary health, pulmonary conditions,pulmonary events or pulmonary-related event, and pulmonary outcomes.

“Pulmonary conditions,” as used herein, may refer to medical, health orother characteristics or attributes associated with a pulmonary state.Pulmonary conditions are descriptions, measurements, diagnoses, etc.which refer or relate to a state, attribute or explanation of a statepertaining to the pulmonary system. A distinguishing point is that apulmonary condition reflects a state of a patient's pulmonary system ata given point in time. It is, however, not an event or occurrenceitself. Without limiting the foregoing, a pulmonary condition may referto an already existing pulmonary condition, a change in state (e.g., anexacerbation or worsening) in or to an existing pulmonary condition,and/or an appearance of a new pulmonary condition. One or more pulmonaryconditions of a user may be used to describe the pulmonary health of theuser.

A “pulmonary event” or “pulmonary-related event,” on the other hand, issomething that has occurred with respect to one's pulmonary system andit may be a contributing, associated or precipitating cause of one ormore pulmonary conditions, but it is the causative reason for the one ormore pulmonary conditions or a contributing or associated reason for theone or more pulmonary conditions. For example, and without limiting theforegoing, a pulmonary-related event may include, without limitation,lung cancer, chronic obstructive pulmonary disease (COPD), asthma,emphysema, pulmonary fibrosis, dyspnoea, bronchitis, bronchiolitis,asbestosis, aspergillosis, pneumothorax, pneumoconiosis,coccidioidomycosis, cystic fibrosis, coronavirus, pneumonia,mesothelioma, pulmonary hypertension, pulmonary embolism, respiratorysyncytial virus, sarcoidosis, tuberculosis, other cold viruses affectingthe pulmonary system, other fungal infections affecting the pulmonarysystem, and the like, and any other pulmonary-related medical conditionsand events, including those which may also be a consequence ofprocedures or interventions (including, without limitation, pulmonaryinterventions) that may negatively affect the health, performance, orpredicted future performance of the pulmonary system or of anyphysiological systems or health-related attributes of a patient wheresuch systems or attributes are themselves affected by the performance ofthe patient's pulmonary system. These pulmonary-related events mayrender individuals, optionally with extant comorbidities, susceptible toa first comorbidity or additional comorbidities or independent medicalproblems such as, without limitation, cardiac issues, congestive heartfailure, fatigue issues, oxygenation issues, vascular issues,cardio-renal anemia syndrome (CRAS), muscle loss issues, enduranceissues, strength issues, sexual performance issues (such as erectiledysfunction), ambulatory issues, obesity issues, reduction of lifespanissues, reduction of quality-of-life issues, and the like. “Issues,” asused in the foregoing, may refer, without limitation, to exacerbations,reductions, mitigations, compromised functionings, eliminations, orother directly or indirectly caused changes in an underlying conditionor physiological organ or psychological characteristic of the individualor the sequelae of any such change, where the existence of at least onesaid issue may result in a diminution of the quality of life for theindividual. The existence of such an at least one issue may itself beremediated by reversing, mitigating, controlling, or otherwiseameliorating the effects of said exacerbations, reductions, mitigations,compromised functionings, eliminations, or other directly or indirectlycaused changes in an underlying condition or physiological organ orpsychological characteristic of the individual or the sequelae of suchchange. In general, when an individual suffers a pulmonary-relatedevent, the individual's overall quality of life may become substantiallydegraded, compared to its prior state.

A “pulmonary intervention” is a process, procedure, surgery, drugregimen or other medical intervention or action undertaken with theintent to minimize the negative effects of a pulmonary-related event(or, if a pulmonary-related event were to have positive effects, tomaximize those positive effects) that has already occurred, that isabout to occur or that is predicted to occur with some probabilitygreater than zero, or to eliminate the negative effects altogether. Apulmonary intervention may also be undertaken before a pulmonary-relatedevent occurs with the intent to avoid the pulmonary-related event fromoccurring or to mitigate the negative consequences of thepulmonary-related event should the pulmonary-related event still occur.

A “pulmonary outcome” may be the result of either a pulmonaryintervention or other treatment or the result of a pulmonary-relatedevent for which no pulmonary intervention or other treatment has beenperformed. For example, if a patient dies from the pulmonary-relatedevent, and the death occurs because of, in spite of, or without anypulmonary interventions, then the pulmonary outcome is the patient'sdeath. On the other hand, if a patient has a pulmonary condition and apulmonary intervention is performed to reduce the effects of thepulmonary condition, then the pulmonary outcome can be significantlyimproved pulmonary health for the patient. Accordingly, a pulmonaryoutcome may generally refer, in some examples, to both negative andpositive outcomes.

Despite the multifarious problems arising out of the foregoingquality-of-life issues, research has shown that exercise rehabilitationprograms can substantially mitigate or ameliorate said issues as well asimprove each affected individual's quality of life. In particular, suchprograms enable these improvements by enhancing aerobic exercisepotential, increasing coronary perfusion, and decreasing both anxietyand depression (which, inter alia, may be present in patients sufferingfrom pulmonary conditions). Moreover, participation in pulmonaryrehabilitation has resulted in demonstrated reductions inre-hospitalizations, in progressions of pulmonary disease, and innegative pulmonary outcomes (e.g., death).

The method 2100 may be performed by processing logic that may includehardware (circuitry, dedicated logic, etc.), software, or a combinationof both. The method 2100 and/or each of the individual functions,subroutines, methods (as the term is used in object-orientedprogramming), or operations may be performed by one or more processingdevices of a computing device (e.g., the computer system 1100 of FIG. 11) implementing the method 2100. For example, a single processing devicemay be configured to perform all of the functions of the method 2100,two or more processors may be configured to perform respective functionsof the method 2100, etc. The method 2100 may be implemented as computerinstructions stored on a memory device and executable by the one or moreprocessing devices. In certain implementations, the method 2100 may beperformed by a single processing thread. Alternatively, the method 2100may be performed by two or more processing threads, each threadimplementing one or more individual functions, routines, subroutines, oroperations of the method. Accordingly, as used herein, “a processingdevice configured to” or “a processor configured to” may be interpretedas a single processing device or processor configured to perform all ofthe recited functions or as two or more processing devices or processorscollectively configured to perform all of the recited functions.Similarly, “circuitry” or “processing circuitry” may be interpreted ascircuitry of one or more processors, processing devices, or otherelectronic circuits configured to respectively or collectively performthe recited functions.

In some embodiments, a system may be used to implement the method 2100.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implementing the method 2100.

At block 2102, the processing device may receive, at a computing device,a first treatment plan designed to treat a pulmonary health issue of auser. The first treatment plan may include at least two exercisesessions that, based on the pulmonary health issue of the user, enablethe user to perform an exercise at different exertion levels. In someexamples, the first treatment plan may be directed to maximizing andoptimizing improvement of pulmonary or overall health of the user whileminimizing discomfort, pain, etc. related to side effects andcomplications subsequent to a pulmonary or other procedure. In otherexamples, the first treatment plan may be directed to preparing the userfor an upcoming (i.e., future) procedure (i.e., prehabilitation),increasing a likelihood that a user will be eligible for the upcomingprocedure, increasing a likelihood of success and/or survival of theupcoming procedure, and/or reducing a likelihood of complications orside effects from the upcoming procedure, etc. In some embodiments,pulmonary information pertaining to the user may be received from anapplication programming interface associated with an electronic medicalrecords system.

In some embodiments, the first treatment plan may be generated based onattribute data including an eating or drinking schedule of the user(including a diet or dietary plan of the user), information pertainingto an age of the user, information pertaining to a sex of the user,information pertaining to a weight of the user, information pertainingto a gender of the user, an indication of a mental state of the user,information pertaining to a genetic condition of the user, informationpertaining to a disease state of the user, information pertaining to amicrobiome from one or more locations on or in the user, an indicationof an energy level of the user, information pertaining to a weight ofthe user, information pertaining to a height of the user, informationpertaining to a body mass index (BMI) of the user, informationpertaining to a family history of cardiovascular health issues of theuser, information pertaining to comorbidities of the user, informationpertaining to desired health outcomes of the user if the treatment planis followed, information pertaining to predicted health outcomes of theuser if the treatment plan is not followed (including variations whereinsome aspects of the treatment plan are followed and other aspects of thetreatment plan are not followed, and wherein a probability may beassociated with the degree to which a treatment plan or an aspectthereof was followed or not followed), or some combination thereof.

The attribute data may further include information associated with theuse of the one or more electromechanical machines to perform one or moretreatment plans, such as which treatment plans the user performed in agiven time period, a frequency and a duration for which the userperformed each of the treatment plans, measurement information (and anychanges, such as improvements, in the measurement information over timeas the user performed the treatment plans) received while the userperformed each of the treatment plans, or some combination thereof. Forexample only, the performance information may be indicative of whetherperforming the treatment plans has resulted in any improvement to (orworsening of) a pulmonary or other condition of the user.

The personal information may include characteristics such as: vital-signor other measurement; performance; demographic; psychographic;geographic; diagnostic; measurement- or test-based; medically historic;behavioral; historic; cognitive; etiologic; cohort-associative;differentially diagnostic; surgical, physically therapeutic, microbiomerelated, recommended pharmacologic and other treatment(s); measurementssuch as arterial blood gas and/or oxygenation levels or percentages;glucose levels; blood oxygen levels; insulin levels; etc.

The attribute data may include measurement information measured before,while, or after the user performs the treatment plan, such as a vitalsign, a respiration rate, a heartrate, a temperature, a blood pressure,a glucose level, arterial blood gas and/or oxygenation levels orpercentages, or other biomarker(s), or some combination thereof.Received cardiovascular data may include a cardiac output of the user, aheartrate of the user, a heart rhythm of the user, a blood pressure ofthe user, a blood oxygen level of the user, a cardiovascular diagnosisof the user, a non-cardiovascular diagnosis of the user, a respirationrate of the user, spirometry data related to the user, or somecombination thereof. Received pulmonary data may include a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a respiration rate of theuser, a pulmonary diagnosis of the user, an oncologic diagnosis of theuser, a pulmonary diagnosis of the user, a pathological diagnosisrelated to a prostate gland or urogenital tract of the user, spirometrydata related to the user, or some combination thereof.

The attribute data may be received from various data sources, including,but not limited to, the treatment apparatus 70 or any component of thesystems described herein, sensors, an electronic medical record system,an application programming interface, a third-party application, or somecombination thereof.

At block 2104, while the user uses an electromechanical machine toperform the first treatment plan for the user, the processing device mayreceive pulmonary data (including any relevant attribute data asdescribed above) from one or more sensors configured to measure thepulmonary data associated with the user. In some embodiments, thepulmonary data may include a weight of the user, a cardiac output of theuser, a heartrate of the user, a heart rhythm of the user, a bloodpressure of the user, a blood oxygen level of the user, a cardiovasculardiagnosis of the user, a non-cardiovascular diagnosis of the user, arespiration rate of the user, a pulmonary diagnosis of the user, anoncologic diagnosis of the user, a pulmonary diagnosis of the user, apathological diagnosis related to a prostate gland or urogenital tractof the user, spirometry data related to the user, or some combinationthereof.

At block 2106, the processing device may transmit the pulmonary data. Insome embodiments, one or more machine learning models 13 may be executedby the server 30 and the machine learning models 13 may be used togenerate a second treatment plan based on the pulmonary data. As usedherein, “second treatment plan” may refer to any of a new treatmentplan, adjustments/modifications to the first treatment plan, the firsttreatment plan as modified in accordance with the machine learningmodels 13, etc. The second treatment plan may modify at least oneexertion level, and the modification may be based on a standardizedmeasure including perceived exertion, pulmonary data, and a pulmonaryhealth issue of the user. In some embodiments, the standardized measureof perceived exertion may include a metabolic equivalent of tasks (MET)or a Borg rating of perceived exertion (RPE). The standardized measuremay be further based on sensed or measured information, user inputs(e.g., feedback), clinician inputs, or some combination thereof.

In some embodiments, the one or more machine learning models generatethe second treatment plan by predicting exercises that will result inthe desired exertion level for each session (e.g., while alsominimizing: discomfort, pain, exacerbation of side effects andcomplications, a likelihood that the user will discontinue the treatmentplan, conflict with comorbid conditions, etc. as described below in moredetail). The one or more machine learning models may be trained using(i) as input data, data pertaining to the standardized measure ofperceived exertion, other users' pulmonary data, and other users'pulmonary health issues, and (ii) as output data, other users' exertionlevels that led to desired results. The input data and the output datamay be labeled and mapped accordingly.

At block 2108, the processing device may receive the second treatmentplan from the server 30. The processing device may implement at least aportion of the treatment plan to cause an operating parameter of theelectromechanical machine to be modified in accordance with the modifiedexertion level set in the second treatment plan. To that end, in someembodiments, the second treatment plan may include a modified parameterpertaining to the electromechanical machine. The modified parameter mayinclude a resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, a velocity, anangular velocity, an acceleration, a torque, or some combinationthereof. The processing device may, based on the modified parameter,control the electromechanical machine.

In some embodiments, transmitting the pulmonary data may includetransmitting the pulmonary data to a second computing device that relaysthe pulmonary data to a third computing device that is associated with ahealthcare professional.

FIG. 21B shows a simplified block diagram of the computer-implementedsystem 10 of FIG. 1 , configured to implement the method 2100 of FIG.21A, a method 2120 of FIG. 21C, etc. Implementing the methods 2100/2120may include using an artificial intelligence and/or machine learningengine to, for pulmonary rehabilitation, generate one or more treatmentplans, recommend treatment plans, and/or provide excluded treatmentplans that should not be recommended to a patient, adjust treatmentplans, etc. as described below in more detail.

The system 10 includes the server 30 configured to store and providedata associated with generating and managing the treatment plan. Theserver 30 may include one or more computers and may take the form of adistributed and/or virtualized computer or computers. The server 30communicates with one or more clinician interfaces 20 (e.g., via thefirst network 34, not shown in FIG. 20B). Although not shown in FIG.22B, the server 30 may further communicate with the supervisoryinterface 90, the reporting interface 92, the assistant interface 94,etc. (referred to collectively, along with the clinician interface 20,as clinician-side interfaces). The processor 36, memory 38, and the AIengine 11 (e.g., implementing the machine learning models 13) areconfigured to implement the methods 2200/2240.

For example, the attribute data (e.g., pulmonary and other attributedata defined above) may be stored in the memory 38 (e.g., along with theother data stored in the data store 44 as described above in FIG. 1 ).The attribute data may be received via the clinician interface 20 and/orother clinician-side interfaces, the patient interface 50 and/or thetreatment apparatus 70 (e.g., via the second network 58), directly fromvarious sensors, etc.

The stored attribute data is accessible by the processor 36 to enablegeneration and/or modification of at least one treatment plan for theuser in accordance with the attribute data. For example, in someembodiments, the processor 36 is configured to execute instructionsstored in the memory 38 and to implement the AI engine 11 to generatethe treatment plan, wherein the treatment plan includes one or moreexercises directed to improving (or increasing a probability ofimproving) pulmonary or overall health subsequent to a pulmonaryprocedure, treatment, or diagnosis, preparing the user for an upcomingpulmonary procedure or treatment, improving eligibility of the user foran upcoming pulmonary procedure or treatment, minimizing pain,discomfort, side-effects, complications, etc. associated with havingundergone a pulmonary procedure or treatment, minimizing interferencewith comorbid conditions, and so on. The treatment plan may specifyparameters including, but not limited to, which exercises to include oromit, intensities of various exercises, limits (e.g., minimumheartrates, maximum heartrates, minimum and maximum exercise speeds(e.g., pedaling rates), minimum and maximum forces or intensitiesexerted by the user, etc.), respective durations and/or frequencies ofthe exercises, adjustments to make to the exercises while the treatmentapparatus is being used to implement the treatment plan, etc.Adjustments to the treatment plan can be performed, as described belowin more detail, at the server 30 (e.g., using the processor 36, the AIengine 11, etc.), the clinician-side interfaces, and/or the treatmentapparatus 70.

The server 30 provides the treatment plan to the treatment apparatus 70(e.g., via the second network 58, the patient interface 50, etc.). Thetreatment apparatus 70 is configured to implement the one or moreexercises of the treatment plan. For example, the treatment apparatus 70may be responsive to commands supplied by the patient interface 50and/or a controller of the treatment apparatus 70 (e.g., the controller72 of FIG. 1 ). In one example, the processor 60 of the patientinterface 50 is configured to execute instructions (e.g., instructionsassociated with the treatment plan stored in the memory 62) to cause thetreatment apparatus 70 to implement the treatment plan. In someexamples, based on information associated with the user and real-timedata (e.g., measurement information, such as sensor or other datareceived while the user is performing the one or more exercises usingthe treatment apparatus), user inputs, etc., the patient interface 50and/or the treatment apparatus 70 may be configured to adjust thetreatment plan and/or individual exercises.

In order to generate the treatment plan, the server 30 according to thepresent disclosure may be configured to execute, using the AI engine 11,one or more ML models 13. For example, the ML models 13 may include, butare not limited to, an attribute data model (or models) 13-1, aprobability model (or models) 13-2, and a treatment plan model (ormodels) 13-3, referred to collectively as the ML models 13. Each of theML models 13 may include different layers of nodes as described above.

Although each of the ML models 13 is shown as a separate model, featuresof each of the ML models 13 may be implemented in a single model or typeof model, such as the treatment plan model 13-3. For example, thetreatment plan model 13-3 may be configured to receive, as input, theattribute data, determine pulmonary or overall health of a user based onthe attribute data, determine eligibility of the user for a pulmonaryprocedure or treatment, determine various probabilities associated withthe attribute data, and generate a treatment plan (e.g., to increase ordecrease respective probabilities) in accordance with the principles ofthe present disclosure.

The attribute data model 13-1 is configured to receive the attributedata (including pulmonary data) and related inputs and, in someexamples, to exclude and add attribute data (e.g., apply filtering tothe attribute data), generate relative weights for the attribute data,and update the attribute data based on external inputs (e.g., receivedfrom the clinician-side interfaces and/or the patient interface 50),etc. The attribute data model 13-1 is configured to output, to theprobability model 13-2, a selected set of the attribute data (referredto herein as “selected attribute data”), which may include weighted ormodified attribute data. In some examples, the attribute data model 13-1may be omitted and the attribute data may be provided directly to theprobability model 13-2 and/or the treatment plan model 13-3.

The probability model 13-2 is configured to determine, based on theselected attribute data received from the attribute data model 13-1,eligibility of the user for a pulmonary procedure or treatment (e.g., aprobability that the user will be considered eligible for the pulmonaryprocedure or treatment) and various other probabilities associated withthe pulmonary and overall health of the user. For example, the variousprobabilities include, but are not limited to, a probability that thepulmonary or overall health of the user will improve or worsen (e.g.,with or without undergoing the pulmonary procedure or treatment, with orwithout performing a treatment plan, etc.), a probability that a desiredgoal of the user (e.g., a target weight or weight loss, the ability toperform a specific physical activity, etc.) will be attained (e.g., withor without undergoing the pulmonary procedure or treatment, with orwithout performing a treatment plan, etc.), a probability of sideeffects or complications subsequent to the pulmonary procedure ortreatment, probabilities that one or more comorbid conditions willimprove or worsen, and so on. Each probability may be dependent upon theselected attribute data and any assigned weights, usage history of thetreatment apparatus 70 by the user, cohort data (as described above),and/or environmental and other external or variable data (e.g., currentair conditions, temperature, climate, season or time of year, time ofday, etc.).

The treatment plan model 13-3 is configured to generate the treatmentplan directed to change one or more of the various probabilities (e.g.,increase probabilities of favorable outcomes/results or decrease aprobabilities of unfavorable results). In some embodiments, thetreatment plan model 13-3 is configured to modify the treatment plan toincrease the probability that the user will be considered eligible forthe pulmonary procedure or treatment; modify the treatment plan toincrease the probability that the pulmonary overall health of the userwill improve or worsen; modify the treatment plan to increase theprobability that a desired goal of the user will be attained; modify thetreatment plan to decrease the probability that side effects,complications, pain or discomfort, etc. associated with the pulmonaryprocedure or treatment will occur; modify the treatment plan to decreasethe probability that one or more comorbid conditions of the user will beexacerbated/aggravated; and combinations thereof.

To increase (or decrease) the various probabilities described above, thetreatment plan may include one or more exercises associated withimproving specific health conditions or characteristics of the user (asindicated by the selected attribute data) contributing to the pulmonaryor overall health of the user, such as improving one or more of:pulmonary conditions of the user; blood pressure, vascularcharacteristics, blood oxygen levels, and/or any other cardiac- orcardiovascular-related condition of the user; oncological conditions ofthe user; orthopedic conditions of the user; weight or BMI of the user;overall physical activity levels of the user; and/or any combination ofany specific condition of the user described herein. For example, ifweight or BMI is determined to be a highest contributor to a probabilitythat pulmonary health will improve, the treatment plan may be configuredspecifically to reduce the BMI of the user. As another example, if highblood pressure is the highest contributor to a low probability that theuser will be eligible for the pulmonary procedure or treatment, thetreatment plan may be configured specifically to reduce the bloodpressure of the user. As still another example, if high blood pressureand risk of side-effects are each determined to be highest contributors(e.g., equal or near equal contributors) to a low probability that theuser will be eligible for the pulmonary procedure or treatment, thetreatment plan may be configured (e.g., balanced) to reduce both theblood pressure of the user and the risk of side effects.

The treatment plan may include, but is not limited to, specific targetexercises for the user to perform using the treatment apparatus 70,suggested replacement or alternative exercises (e.g., in the event thatthe user is unable to perform one or more of the target exercises due todiscomfort), parameters/limits for each of the exercises (e.g.,duration, intensity, repetitions, etc.), and excluded exercises (e.g.,exercises that should not be performed by the user). In some examples,rather than including only specific exercises, the treatment plan mayinclude one or more exercise parameters (e.g., resistance or force,intensity, range of motion, etc.) or user conditions/measurements (e.g.,heartrates, breathing rates or respiratory behavior, METcharacteristics, specific movements, weight loss, etc.) associated withimproving a health condition. For example, the treatment plan mayspecify one or more desired ranges of values for various characteristics(e.g., a heartrate range).

The treatment plan model 13-3 may be further configured to generate,based on the various probabilities, a recommendation regarding whetherthe user should undergo a pulmonary procedure or treatment. In someexamples, the recommendation may be binary (e.g., a value indicating“yes” or “no”). In other examples, the recommendation may include ascore or ranked value (e.g., a score between 1 and 100, where “1” is aminimum recommendation and a “100” is a maximum recommendation). Instill other examples, the recommendation may include variousrecommendation tiers (which may be based on a corresponding scorebetween 1 and 100), such as “strongly recommend against,” “recommendagainst,” “recommend for,” and “strongly recommend for.” Therecommendation may be provided to the user via the patient interface 50of the treatment apparatus, the clinician interface 20, etc.

In some examples, the recommendation may be based on a simple, directcomparison between the probability that the pulmonary procedure ortreatment will improve the pulmonary or overall health of the user and aprobability threshold. In other examples, as described above, therecommendation may be based on a more complex analysis including: thecomparison between the probability and the probability threshold; aprobability that the pulmonary or overall health of the user willimprove without the undergoing the pulmonary procedure or treatment; aprobability that the pulmonary procedure will result in worsening ofcomorbid conditions; or combinations thereof.

In still other examples, the recommendation may include information thatindicates progress toward one or more goals related to satisfyingeligibility requirements for the pulmonary procedure or treatment. Forexample, a recommendation against undergoing the one or more proceduresmay be based on specific components of the attribute data being below acorresponding threshold. Accordingly, the recommendation may include agoal for the specific components in order to qualify for the pulmonaryprocedure or treatment, a progress (e.g., as a percentage or otherindicator)) toward the goal, etc. As one example, a recommendationagainst the pulmonary procedure or treatment may be based on a weight orBMI being greater than a threshold. The recommendation may include agoal for the weight or BMI (e.g., below a threshold value) in order toqualify for the pulmonary procedure or treatment, as well as a progresstoward that goal (e.g., 50% of the overall weight reduction achieved).Based on the one or more goals and/or progress toward the one or moregoals, the recommendation may include modifications to the treatmentplan.

FIG. 21C illustrates an example method 2120 for generating a treatmentplan for pulmonary rehabilitation and/or prehabilitation according tothe present disclosure. The method 2120 expands upon the method 2100described above in FIG. 21A with additional details described above inFIG. 21B. The system 10 described in FIG. 21B may be configured toperform the method 2120. As described herein, generating the treatmentplan for pulmonary rehabilitation and/or prehabilitation may includegenerating the treatment plan to increase or decrease variousprobabilities associated with eligibility of the user for a pulmonaryprocedure or treatment, prehabilitation of the user in preparation for apulmonary procedure or treatment, rehabilitation subsequent to apulmonary procedure and/or treatment, rehabilitation subsequent to theuser having a pulmonary condition or diagnosis, etc. While describedbelow with respect to increasing or decreasing the variousprobabilities, in some examples systems and methods may be configured togenerate the treatment plan to more generally improve pulmonary oroverall health without calculating and/or being responsive to specificprobabilities.

At 2122, the system 10 (e.g., the attribute data model 13-1) receivesthe attribute data, which includes pulmonary data and other datadescribed above. The attribute data may include both non-modifiable andstatic characteristics associated with the user and modifiable ordynamic characteristics associated with the user. Non-modifiablecharacteristics may include, but are not limited to, genetic factors,family history, age, sex, cardiac history, comorbidities, diabetichistory, oncological history (e.g., whether the user has previouslyundergone chemotherapy and/or radiation treatment), etc. Modifiablecharacteristics may include, but are not limited to, heartrate, bloodpressure, current diabetic status, blood oxygen (SpO2) levels,cholesterol, weight, diet, lipid levels in the blood, measurementsdetermined by any blood test, biopsy or radiologic test, tobacco use,alcohol use, current medications, blood pressure, physical activitylevel, psychological factors (e.g., depression or anxiety), etc.

The attribute data may include performance information. The performanceinformation may include, inter alia, information associated with the useof the one or more electromechanical machines to perform one or moretreatment plans. In other words, the method 2120 may be implementedwhile the user has already been performing a previously generatedtreatment plan (e.g., for a period of weeks, months, etc.). In someexamples, the treatment plan may correspond to a treatment planprescribed to the user in preparation for a pulmonary procedure ortreatment, a treatment plan prescribed to the user for rehabilitationsubsequent to the procedure or treatment, etc. Accordingly, in someexamples, the method 2120 may correspond to an assessment of progress ofthe user toward a goal of qualifying for the procedure or treatment anda recommendation of whether (and, if so, when) to undergo the procedureor treatment.

At 2124, the system 10 (e.g., the attribute data model 13-1, as executedby the AI engine 11, the processor 36, etc.) generates and outputs aselected set of attribute data. In some examples, the selected set ofattribute data comprises of all received attribute data. In otherexamples, the attribute data model 13-1 applies filtering to theattribute data (e.g., to exclude certain user characteristics that maynot contribute to the various probabilities calculated by the method2220), or applies weights to or ranks (e.g., assigns a priority valueto) components of the attribute data, etc. As one example, somecomponents of the attribute data may have a greater correlation withpulmonary rehabilitation or prehabilitation. Conversely, othercomponents of the attribute data may have a lesser correlation withpulmonary rehabilitation or prehabilitation. Some components of theattribute data may be binary (i.e., simply present or not present, suchas diabetic history) and may be assigned a binary weight such as 0 or 1while other components of the attribute data may have a variablecontribution to the various probabilities, such as blood pressure, andmay be assigned a decimal value between 0 and 1. Components of theattribute data that are determined to have a stronger than averagecorrelation with pulmonary rehabilitation or prehabilitation may beassigned a weight greater than 1 (1.1, 1.5, 2.0, etc.). In someexamples, the attribute data model 13-1 may be configured to categorizecomorbid conditions indicated in the attribute data as described belowin more detail.

At 2126, the system 10 (e.g., the probability model 13-2, as executed bythe AI engine 11, the processor 36, etc.) receives the selectedattribute data and, based on the selected attribute data and associatedweights and/or ranking, calculates an eligibility of the user for apulmonary procedure or treatment and various other probabilitiesassociated with the pulmonary and overall health of the user asdescribed above, such as a probability that the pulmonary or overallhealth of the user will improve or worsen, a probability that a desiredgoal of the user associated with pulmonary health will be attained, aprobability of side effects or complications subsequent to a procedureor treatment, probabilities that one or more comorbid conditions willimprove or worsen, and so on. In some examples, the probability model13-2 may further modify or adjust (e.g., increase or decrease, exclude,etc.), based on additional data, such as environmental data or othervariable data as described, any of the attribute data. For example, somecharacteristics contained in the attribute data may be exacerbated byconditions such as air conditions in a geographic region associated withthe user, climate, etc. This adjustment of the attribute data may alsobe performed using the attribute data model 13-1.

The probability model 13-2 calculates each of the various probabilitiesas a probability value or values, a confidence interval, anon-probabilistic value, a numerical value, etc. As one example, theprobability values may correspond to Bayesian probabilities, Markovianprobabilities, a stochastic prediction, a deterministic prediction, etc.Each of the probability values may be calculated based on a combinationof components of the attribute data and respective weights/valuesprovided by the attribute data model 13-1. For example, a probabilityvalue may be calculated based on a respective probability of improving aspecific pulmonary or other condition associated with each component ofthe attribute data. In other words, each component of the attribute datamay have an associated probability or contribution to a givenprobability. By using all of the probability values of the attributedata in the received set of attribute data, the probability model 13-2may calculate an overall probability associated with the pulmonaryhealth of the user. In one example, the respective probabilities of eachof the components of the attribute data may be weighted.

At 2128, in some embodiments, the system 10 (e.g., the treatment planmodel 13-3, as executed by the AI engine 11, the processor 36, etc.)receives the probabilities determined at 2226 and, based on theprobabilities, generates one or more recommendations regarding whetherthe user should undergo a pulmonary procedure or treatment (e.g.,recommendations for or against undergoing the procedure or treatment).The recommendations may include one or more of: a binary “yes” or “no”recommendation; a score or ranked recommendation value; multiple scoresor recommendations for the procedure or treatment; information thatindicates progress toward one or more goals related to qualifying forthe procedure or treatment; modifications to the treatment plan based onthe one or more goals and/or progress toward the one or more goals; etc.

In some examples, the recommendation is generated based only on acomparison between a probability of the procedure or treatment improvingthe pulmonary or overall health of the user and a probability threshold.In other examples, the recommendation may be based on both thecomparison between the probability and the probability threshold and acomparison between the probability and a probability that the pulmonaryor overall health of the user will improve without undergoing theprocedure or treatment.

At 2130, the method 2120 (e.g., the treatment plan model 13-3, asexecuted by the AI engine 11, the processor 36, etc.) receives theprobabilities calculated by the probability model 13-2 and modifies thetreatment plan accordingly (i.e., based on the probabilities and, insome examples, the recommendations generated at 2228). For example, inresponse to a recommendation that the user undergoes a pulmonaryprocedure or treatment, the treatment plan model 13-3 may modify thetreatment plan to prepare the user for the procedure or treatment, toprepare the user for rehabilitation subsequent to the procedure ortreatment, etc. Conversely, in response to a recommendation that theuser does not undergo the procedure or treatment, the treatment planmodel 13-3 may modify the treatment plan to improve the eligibility ofthe user for the procedure or treatment (e.g., improve aprobability/likelihood that the user will qualify for the procedure ortreatment in a subsequent assessment), to improve the pulmonary oroverall health of the user without undergoing the procedure ortreatment, etc. In some examples, the treatment plan model 13-3receives, in addition to the calculated probabilities andrecommendations, the attribute data.

To improve eligibility of the user for the procedure or treatment (e.g.,increase the probability that the user will qualify for the procedure ortreatment in a subsequent assessment), the treatment plan may includeone or more exercises or exercise routines associated with improving oneor more pulmonary or other health conditions of the user associated witheligibility. The treatment plan may include, but is not limited to,specific target exercises for the user to perform using the treatmentapparatus 70, suggested replacement or alternative exercises,parameters/limits for each of the exercises, and/or excluded exercises.

As one example, the treatment plan model 13-3 may generate the treatmentplan in accordance with generalized parameters associated with improvingone or more specific health conditions associated with eligibility forthe pulmonary procedure or treatment. For example, for a specific one ormore of the treatment apparatuses 70 being used with the system 10, thetreatment plan may specify parameters including, but not limited to, oneor more exercises (e.g., in systems where the treatment apparatus 70 isconfigured to implement more than one exercise, in systems with multipletreatment apparatuses, etc.) to perform, a frequency of each exercise, aduration of each exercise, settings for the treatment apparatus 70during the exercise (e.g., resistance, intensity, speed, slope/gradient,etc.), and desired ranges for various measured, sensed, and/orcalculated characteristics of the user while the user performs thetreatment plan (e.g., heartrate).

In still other examples, parameters of the treatment plan or one or moreexercises may be limited or, based on specific attribute data, exercisesmay be excluded. For example, the presence of one or more components ofthe attribute data may increase the probability of discomfort,discontinued use, injury to the user, etc. Accordingly, the treatmentplan may limit the value of parameters relating to characteristics suchas intensity, frequency, duration, etc.

In still another example, the system 10 may be configured to generate atreatment plan configured to manage other health conditions, riskfactors, etc. The system 10 may be further configured to adjust thetreatment plan to improve the eligibility while also targeting the otherhealth conditions. For example, the treatment plan may include one ormore exercises, parameters, etc. directed to improving a first healthcondition. The system 10 may add or omit exercises, extend or limitdesired ranges of operating parameters and/or measured usercharacteristics, etc. to improve the probability of the user's beingeligible for the procedure or treatment, all while still targeting thefirst health condition.

Similarly, for prehabilitation prior to the pulmonary procedure ortreatment (i.e., to prepare the user for the procedure or treatment),the treatment plan may include one or more exercises or exerciseroutines associated with improving one or more pulmonary or other healthconditions of the user associated with a desired outcome of theprocedure or treatment. As one example, the treatment plan model 13-3may generate the treatment plan in accordance with generalizedparameters associated with improving one or more specific pulmonary orother health conditions associated with an outcome of the procedure ortreatment. In still other examples, parameters of the treatment plan orone or more exercises may be limited or, based on specific attributedata, exercises may be excluded. In still another example, whilepreparing the user for the pulmonary procedure, the system 10 may beconfigured to generate a treatment plan configured to manage otherhealth conditions, risk factors, etc.

To improve pulmonary or overall health subsequent to the procedure(i.e., for rehabilitation) or to improve pulmonary or overall health fora user that did not undergo the procedure or treatment, the treatmentplan may include one or more exercises or exercise routines associatedwith improving one or more pulmonary or other health conditions of theuser (e.g., increasing a probability that the pulmonary or overallhealth of the user will improve). As one example, the treatment planmodel 13-3 may generate the treatment plan in accordance withgeneralized parameters associated with improving one or more specificpulmonary or other health conditions. In still other examples,parameters of the treatment plan or one or more exercises may be limitedor, based on specific attribute data, exercises may be excluded. Instill another example, the system 10 may be configured to generate atreatment plan configured to manage other health conditions, riskfactors, etc. while performing rehabilitation subsequent to theprocedure or treatment.

In some examples, the treatment plan may be configured to increase aprobability that a desired goal of the user associated with pulmonaryhealth will be attained (e.g., prior to and/or subsequent to theprocedure or treatment, in lieu of undergoing the procedure ortreatment, etc.). For example, the attribute data may include anindication of one or more goals of the user as input by the user, aclinician, etc. The goals may correspond to specific physical activitiesthat may be limited by one or more pulmonary conditions (e.g., swimming,walking a predetermined distance, participating in a specific sport orrecreational activity, travel (e.g., air travel), etc.), and/or thegoals may correspond to specific physical attributes (e.g., a targetweight, BMI, blood pressure, etc.). The treatment plan model 13-3 maygenerate the treatment plan in accordance with generalized parametersassociated with attaining the one or more goals. Parameters of thetreatment plan or one or more exercises may be limited or, based onspecific attribute data, exercises may be excluded. The system 10 may beconfigured to generate a treatment plan configured to manage otherhealth conditions, risk factors, etc. while increasing the probabilitythat the user will attain the one or more goals.

In any of the above examples, the treatment plan may be furtherconfigured to perform rehabilitation and/or prehabilitation (e.g.,increase probabilities of desirable outcomes associated with improvedpulmonary or overall health) while decreasing a probability of sideeffects or complications subsequent to the procedure or treatment,increasing (or decreasing) probabilities that one or more comorbidconditions will improve (or worsen), and so on. Comorbid conditions(which, as used herein for simplicity, may also comprise side effects orcomplications associated with the procedure or treatment as describedabove) may interfere with rehabilitation and prehabilitation. Forexample: pain, discomfort, physical limitations, etc. associated withcomorbid conditions may limit the ability of the user to perform thetreatment plan, cause the user to avoid performing the treatment plan,etc.; performing the treatment plan may worsen or cause (e.g., increasethe risk of occurrence of) comorbid conditions; comorbid conditions maydecrease the effectiveness of the treatment plan; etc. Accordingly,generating (and implementing) the treatment plans according to thepresent disclosure may include managing, compensating for, andpreventing complications caused by comorbid conditions duringrehabilitation and prehabilitation.

In accordance with the principles of the present disclosure, generatingand/or modifying the treatment plan may include generating and/ormodifying the treatment plan based on one or more health conditions orother criteria associated with pulmonary health, including, but notlimited to: exacerbations/comorbidities; dyspnoea/fatigue; fear offeeling breathless (suitable respiratory capacity, spirometry data;respiratory exertion threshold; shortness of breath); frailty; exposureto cold or humid weather; exposure to smoke or other environmentalfactors; systemic inflammation (e.g., inflammation caused by obesity);osteoporosis; lung cancer; mood. disorders (e.g., depression, anxiety)associated with pulmonary disease; sleep disorders (e.g., obstructivesleep apnea); and/or combinations thereof. For example, the treatmentplan may be modified to further treat, compensate for, etc. healthconditions caused by or otherwise correlated with pulmonary healthconditions as described above.

In one example, the treatment plan may be modified (e.g., by the MLmodels 13) in accordance with one or more barriers or challengesassociated with pulmonary rehabilitation, such as dyspnoea (e.g.,shortness of breath and/or a perception, by the user, of shortness ofbreath). For example, fear of experiencing shortness of breath may beassociated with reduced exertion levels, reduced duration of treatmentplan sessions, discontinuation of the treatment plan, etc. Accordingly,the treatment plan may be modified to prevent or preempt dyspnoea in theuser.

As one example, the method 2120 is configured to detect a respiratoryexertion level at which the user experiences dyspnoea or shortness ofbreath. The method 210 may detect the respiratory exertion level basedon a user input (e.g., one or more inputs received in response to aprompt on the user interface inquiring whether the user is experiencingshortness of breath, such as inputs received over a plurality ofsessions of the treatment plan) and/or other attribute data, a heartraterate associated with shortness of breath, a breathing rate associatedwith shortness of breath, spirometry data, measures of expiratory andinspiratory volume, number of respirations per second, etc. While theuser performs the treatment plan using the treatment apparatus 70,various characteristics of the user indicative of the respiratoryexertion level are monitored and the treatment plan is modified toprevent the user from reaching the respiratory exertion level. Forexample, if the dyspnoea experienced by the user is associated with aparticular heartrate (“a dyspnoea heartrate”), the method 2120 maymodify the treatment plan, exercise parameters, etc. to prevent the userfrom reaching the dyspnoea heartrate (e.g., by terminating the treatmentplan session, reducing exertion levels, etc. in response to the userreaching a heartrate a predetermined amount below the dyspnoea heartrateor other threshold associated with dyspnoea).

In this manner, the method 2120 and the ML models 13 are configured todynamically modify the treatment plan to ensure that the user does notreach a level of respiratory exertion associated with dyspnoea whileperforming the treatment plan. In particular, the method 2120 modifiesthe treatment plan (e.g., specific exercise parameters of the treatmentplan) to prevent the user from reaching a respiratory exertion or otherdyspnoea threshold. Modifying the treatment plan may include adjusting(e.g., raising or lowering) the respiratory exertion threshold based onperformance information of the user measured or received while the userperforms the treatment plan. For example, the respiratory exertionthreshold may be decreased in response to the user indicating thatdyspnoea occurred (i.e., based on user input), in response to a measureduser characteristic (e.g., spirometry data, heartrate, breathing rate,etc.) reaching a value associated with the respiratory exertionthreshold in an exercise session, and so on. Conversely, the respiratoryexertion threshold may be increased in response to the user notexceeding or reaching the respiratory exertion threshold in apredetermined number of treatment sessions, not exceeding apredetermined offset below the respiratory exertion threshold, etc. Inthis manner, the method 2120 is configured to modify the treatment planto ensure that the user reaches exertion levels that will improvepulmonary health without reaching exertion levels that will causedyspnoea and discourage the user from performing the treatment plan.

The method 2120 may further generate or modify the treatment plan basedon cohort data (e.g., attribute data, treatment plan results, etc.associated with other users in a same cohort as the user, where suchother users have also had and/or prepared for a pulmonary procedure ortreatment). For example, as users in a cohort perform rehabilitation orprehabilitation over time, the models 13 may be updated in view ofpulmonary or overall health conditions that improved as a result ofvarious exercises or other treatment plan parameters, etc. Accordingly,rather than modifying the treatment plan simply based on the attributedata and treatment plan results of the user, the method 2220alternatively or further generates/modifies the treatment plan furtherbased on the cohort data. For example, thresholds or ranges forparameters, sensed characteristics, etc. may be further limited (i.e.,made more restrictive) or increased (i.e., made less restrictive) inview of cohort data indicating that certain pulmonary health conditionsworsened or improved while users performed the treatment plan. In someexamples, the cohort data includes dyspnoea data indicative of whenvarious users experienced dyspnoea during performance of respectivetreatment plans (e.g., at which respiratory exertion levels usersexperienced dyspnoea in accordance with any of the examples describedabove). In this manner, the treatment plan may be modified based ondyspnoea data of other users.

At 2132, the system 10 (e.g., the patient interface 50 and/or thetreatment apparatus 70) implements the one or more exercises of thetreatment plan. For example, the treatment plan is transmitted to thepatient interface 50 to enable the treatment apparatus 70 to implementthe one or more exercises, and the user initiates the one or moreexercises using the patient interface 50, etc. In some examples, basedon real-time data, the system 10 (e.g., the patient interface 50 and/orthe treatment apparatus 70) optionally adjusts the one or more exercisesbeing implemented. For example, the treatment apparatus 70, the patientinterface 50, and/or other components of the system 10 receive, from oneor more sensors, one or more measurements associated with the user. Theone or more measurements may be received while the user performs thetreatment plan. Example adjustments include, but are not limited to,increasing or decreasing intensity or other parameters to increase ordecrease heartrate, metabolic equivalent of task (MET, a ratio ofworking metabolic rate to resting metabolic rate, as defined here andreferenced elsewhere herein), etc. For example, the adjustments may bemade to maintain a heartrate of the user within a target range (i.e.,without decreasing below a lower limit or increasing above an upperlimit) configured to increase or decrease any of the variousprobabilities described above.

At 2134, the system 10 (e.g., the patient interface 50 and/or thetreatment apparatus 70) determines whether a current session of thetreatment plan has been completed. If true, the method 2120 proceeds to2136. If false, the method 2120 proceeds to 2132. At 2136, based on thecompleted treatment plan session, the system 10 (e.g., the server 30,implementing the models 13) updates or modifies the treatment planand/or attribute data. For example, the treatment plan model 13-3 mayadd exercises to or remove exercises from the treatment plan, adjustparameters of exercises, change the frequency or duration of exercises,etc. As one example, the treatment plan model 13-3 may reduceintensities in response to a determination that the heartrate of theuser exceeded the target range, increase intensities in response to adetermination that the heartrate of the user did not reach the targetrange, increase or decrease the target range, etc. In some examples, tolimit maximum heartrate and rate of heartrate increase while stillincreasing (or decreasing) any of the various probabilities describedabove, the treatment plan model 13-3 may be adjusted based on previousimplementations. In some examples, any modification of the treatmentplan must be approved by a healthcare professional (e.g., via theclinician-side interfaces) prior to enablement of implementation by thetreatment apparatus 70.

[The below clauses are from the original 15200 application as filed.Additional clauses will be added to reflect the claims of the presentapplication upon Vegas approval of the claims]

Clauses

Clause 1.6 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user performs a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, at a computing device, a first treatment plan designed        to treat a pulmonary health issue of a user, wherein the first        treatment plan comprises at least two exercise sessions that,        based on the pulmonary health issue of the user, enable the user        to perform an exercise at different exertion levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive pulmonary data from        one or more sensors configured to measure the pulmonary data        associated with the user;    -   transmit the pulmonary data, wherein one or more machine        learning models are used to generate a second treatment plan;        wherein the second treatment plan modifies at least one exertion        level, and the modification is based on a standardized measure        comprising perceived exertion; the pulmonary data; and the        pulmonary health issue of the user; and    -   receive the second treatment plan.

Clause 2.6 The computer-implemented system of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof, and the computer-implemented system further comprises:

-   -   based on the modified parameter, controlling the        electromechanical machine.

Clause 3.6 The computer-implemented system of any clause herein, whereinthe standardized measure of perceived exertion comprises a metabolicequivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

Clause 4.6 The computer-implemented system of any clause herein,wherein, by predicting exercises that will result in the desiredexertion level for each session, the one or more machine learning modelsgenerate the second treatment plan, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' pulmonary data, and other users'pulmonary health issues.

Clause 5.6 The computer-implemented system of any clause herein, whereinthe first treatment plan is generated based on attribute data comprisingan eating or drinking schedule of the user, information pertaining to anage of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

Clause 6.6 The computer-implemented system of any clause herein, whereinthe transmitting the pulmonary data further comprises transmitting thepulmonary data to a second computing device that relays the pulmonarydata to a third computing device associated with a healthcareprofessional.

Clause 7.6 The computer-implemented system of any clause herein, whereinthe pulmonary data comprises a weight of the user, a cardiac output ofthe user, a heartrate of the user, a heart rhythm of the user, a bloodpressure of the user, a blood oxygen level of the user, a cardiovasculardiagnosis of the user, a non-cardiovascular diagnosis of the user, arespiration rate of the user, spirometry data related to the user, apulmonary diagnosis of the user, an oncologic diagnosis of the user, abariatric diagnosis of the user, a pathological diagnosis related to aprostate gland or urogenital tract of the user, or some combinationthereof.

Clause 8.6 A computer-implemented method comprising:

-   -   receiving, at a computing device, a first treatment plan        designed to treat a pulmonary health issue of a user, wherein        the first treatment plan comprises at least two exercise        sessions that, based on the pulmonary health issue of the user,        enable the user to perform an exercise at different exertion        levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receiving pulmonary data from        one or more sensors configured to measure the pulmonary data        associated with the user, wherein the electromechanical machine        is configured to be manipulated by a user while the user        performs the first treatment plan;    -   transmitting the pulmonary data, wherein one or more machine        learning models are used to generate a second treatment plan;        wherein the second treatment plan modifies at least one exertion        level, and the modification is based on a standardized measure        comprising perceived exertion; the pulmonary data; and the        pulmonary health issue of the user; and    -   receiving the second treatment plan.

Clause 9.6 The computer-implemented method of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof, and the computer-implemented system further comprises:

-   -   based on the modified parameter, controlling the        electromechanical machine.

Clause 10.6 The computer-implemented method of any clause herein,wherein the standardized measure of perceived exertion comprises ametabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE).

Clause 11.6 The computer-implemented method of any clause herein,wherein, by predicting exercises that will result in the desiredexertion level for each session, the one or more machine learning modelsgenerate the second treatment plan, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' pulmonary data, and other users'pulmonary health issues.

Clause 12.6 The computer-implemented method of any clause herein whereinthe first treatment plan is generated based on attribute data comprisingan eating or drinking schedule of the user, information pertaining to anage of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

Clause 13.6 The computer-implemented method of any clause herein,wherein the transmitting the pulmonary data further comprisestransmitting the pulmonary data to a second computing device that relaysthe pulmonary data to a third computing device associated with ahealthcare professional.

Clause 14.6 The computer-implemented method of any clause herein,wherein the pulmonary data comprises a weight of the user, a cardiacoutput of the user, a heartrate of the user, a heart rhythm of the user,a blood pressure of the user, a blood oxygen level of the user, acardiovascular diagnosis of the user, a non-cardiovascular diagnosis ofthe user, a respiration rate of the user, spirometry data related to theuser, a pulmonary diagnosis of the user, an oncologic diagnosis of theuser, a bariatric diagnosis of the user, a pathological diagnosisrelated to a prostate gland or urogenital tract of the user, or somecombination thereof.

Clause 15.6 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive a first treatment plan designed to treat a pulmonary        health issue of a user, wherein the first treatment plan        comprises at least two exercise sessions that, based on the        pulmonary health issue of the user, enable the user to perform        an exercise at different exertion levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive pulmonary data from        one or more sensors configured to measure the pulmonary data        associated with the user, wherein the electromechanical machine        is configured to be manipulated by a user while the user        performs the first treatment plan;    -   transmit the pulmonary data, wherein one or more machine        learning models are used to generate a second treatment plan;        wherein the second treatment plan modifies at least one exertion        level, and the modification is based on a standardized measure        comprising perceived exertion; the pulmonary data; and the        pulmonary health issue of the user; and    -   receive the second treatment plan.

Clause 16.6 The computer-readable medium of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof, and the computer-implemented system further comprises:

-   -   based on the modified parameter, controlling the        electromechanical machine.

Clause 17.6 The computer-readable medium of any clause herein, whereinthe standardized measure of perceived exertion comprises a metabolicequivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

Clause 18.6 The computer-readable medium of any clause herein, wherein,by predicting exercises that will result in the desired exertion levelfor each session, the one or more machine learning models generate thesecond treatment plan, and the one or more machine learning models aretrained using data pertaining to the standardized measure of perceivedexertion, other users' pulmonary data, and other users' pulmonary healthissues.

Clause 19.6 The computer-readable medium of any clause herein, whereinthe first treatment plan is generated based on attribute data comprisingan eating or drinking schedule of the user, information pertaining to anage of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

Clause 20.6 The computer-readable medium of any clause herein, whereinthe transmitting the pulmonary data further comprises transmitting thepulmonary data to a second computing device that relays the pulmonarydata to a third computing device associated with a healthcareprofessional.

Clause 21.6 A computer-implemented system, comprising:

-   -   one or more processing devices configured to    -   receive attribute data associated with a user,    -   generate, based on a pulmonary condition of the user, a selected        set of the attribute data,    -   determine, based on the selected set of the attribute data, a        first probability of improving a pulmonary condition of the user        subsequent to at least one of a pulmonary procedure being        performed on the user, a pulmonary treatment being performed on        the user, and a pulmonary diagnosis, and    -   generate, based on the first probability, a treatment plan that        includes one or more exercises directed to modifying the first        probability; and    -   a treatment apparatus configured to enable implementation of the        treatment plan.

Clause 22.6 The computer-implemented system any clause herein, whereinthe attribute data includes data associated with a pulmonary health ofthe user.

Clause 23.6 The computer-implemented system any clause herein, whereinthe one or more processing devices are configured to execute anattribute data model, and wherein, to generate the selected set of theattribute data, the attribute model is configured to at least one ofassign weights to the attribute data, rank the attribute data, andfilter the attribute data.

Clause 24.6 The computer-implemented system any clause herein, whereinthe one or more processing devices are configured to execute aprobability model, wherein the probability model is configured todetermine the first probability.

Clause 25.6 The computer-implemented system any clause herein, whereinthe one or more processing devices are configured to execute a treatmentplan model, wherein the treatment plan model is configured to generatethe treatment plan to modify the first probability.

Clause 26.6 The computer-implemented system any clause herein, whereinthe one or more processing devices are further configured, based on theselected set of the attribute data, to generate a second probabilitythat the user will be eligible for the at least one of the pulmonaryprocedure and the pulmonary treatment.

Clause 27.6 The computer-implemented system any clause herein, whereinthe one or more processing devices are configured to at least one of (i)generate the treatment plan further to modify the second probability and(ii) generate a recommendation of whether the user should undergo the atleast one of the pulmonary procedure and the pulmonary treatment.

Clause 28.6 The computer-implemented system any clause herein, wherein,subsequent to implementing the treatment plan using the treatmentapparatus, the one or more processing devices are configured, based onthe recommendation, to modify the treatment plan.

Clause 29.6 The computer-implemented system any clause herein, whereinthe one or more processing devices are configured to transmit themodified treatment plan to cause the treatment apparatus to implement atleast one modified exercise of the modified treatment plan.

Clause 30.6 The computer-implemented system any clause herein, wherein,while the user performs the treatment plan, the one or more processingdevices are configured to initiate a telemedicine session between acomputing device of the user and a computing device of a healthcareprofessional.

Clause 31.6 The computer-implemented system any clause herein, whereinthe one or more processing devices are configured, based on arespiratory exertion threshold associated with dyspnoea experienced bythe user, to modify the treatment plan.

Clause 32.6 The computer-implemented system any clause herein, whereinthe one or more processing devices are configured, based on performanceinformation indicative of characteristics of the user measured while theuser performs the treatment plan, to modify the respiratory exertionthreshold.

Clause 33.6 A method, comprising:

-   -   using one or more processing devices,    -   receiving attribute data associated with a user,    -   generating, based on a pulmonary condition of the user, a        selected set of the attribute data,    -   determining, based on the selected set of the attribute data, a        first probability of improving a pulmonary condition of the user        subsequent to at least one of a pulmonary procedure being        performed on the user, a pulmonary treatment being performed on        the user, and a pulmonary diagnosis, and    -   generating, based on the first probability, a treatment plan        that includes one or more exercises directed to modifying the        first probability; and    -   using a treatment apparatus to implement the treatment plan.

Clause 34.6 The method of any clause herein, wherein the attribute dataincludes data associated with a pulmonary health of the user.

Clause 35.6 The method of any clause herein, further comprising, usingthe one or more processing devices, executing an attribute data model,and wherein, to generate the selected set of the attribute data, theattribute model at least one of assigns weights to the attribute data,ranks the attribute data, and filters the attribute data.

Clause 36.6 The method of any clause herein, further comprising, usingthe one or more processing devices, executing a probability model,wherein the probability model is configured to determine the firstprobability.

Clause 37.6 The method of any clause herein, further comprising, usingthe one or more processing devices, executing a treatment plan model,wherein the treatment plan model is configured to generate the treatmentplan to modify the first probability.

Clause 38.6 The method of any clause herein, further comprising, usingthe one or more processing devices, generating, based on the selectedset of the attribute data, a second probability that the user will beeligible for the at least one of the pulmonary procedure and thepulmonary treatment.

Clause 39.6 The method of any clause herein, further comprising, usingthe one or more processing devices, at least one of (i) generating thetreatment plan further to modify the second probability and (ii)generating a recommendation of whether the user should undergo the atleast one of the pulmonary procedure and the pulmonary treatment.

Clause 40.6 The method of any clause herein, further comprising, usingthe one or more processing devices subsequent to implementing thetreatment plan using the treatment apparatus, modifying, based on therecommendation, the treatment plan.

Clause 41.6 The method of any clause herein, further comprising, usingthe one or more processing devices, transmitting the modified treatmentplan to cause the treatment apparatus to implement at least one modifiedexercise of the modified treatment plan.

Clause 42.6 The method of any clause herein, further comprising, whilethe user performs the treatment plan, using the one or more processingdevices to initiate a telemedicine session between a computing device ofthe user and a computing device of a healthcare professional.

Clause 43.6 The method of any clause herein, further comprising usingthe one or more processing devices to, based on a respiratory exertionthreshold associated with dyspnoea experienced by the user, modify thetreatment plan.

Clause 44.6 The method of any clause herein, further comprising usingthe one or more processing devices to, based on performance informationindicative of characteristics of the user measured while the userperforms the treatment plan, modify the respiratory exertion threshold.

System and Method for Using AI/ML and Telemedicine for Cardio-OncologicRehabilitation Via an Electromechanical Machine

FIG. 22 generally illustrates an example embodiment of a method 2200 forusing artificial intelligence and machine learning and telemedicine toperform cardio-oncologic rehabilitation via an electromechanical machineaccording to the principles of the present disclosure. The method 2200may be performed by processing logic that may include hardware(circuitry, dedicated logic, etc.), software, or a combination of both.The method 2200 and/or each of their individual functions, subroutines,or operations may be performed by one or more processing devices of acomputing device (e.g., the computer system 1100 of FIG. 11 )implementing the method 2200. The method 2200 may be implemented ascomputer instructions stored on a memory device and executable by theone or more processing devices. In certain implementations, the method2200 may be performed by a single processing thread. Alternatively, themethod 2200 may be performed by two or more processing threads, eachthread implementing one or more individual functions, routines,subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 2200.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 2200.

At block 2202, the processing device may receive, at a computing device,a first treatment plan designed to treat a cardio-oncologic health issueof a user. The first treatment plan may include at least two exercisesessions that, based on the cardio-oncologic health issue of the user,enable the user to perform an exercise at different exertion levels. Insome embodiments, cardiac and/or oncologic information pertaining to theuser may be received from an application programming interfaceassociated with an electronic medical records system.

In some embodiments, the first treatment plan may be generated based onattribute data including an eating or drinking schedule of the user,information pertaining to an age of the user, information pertaining toa sex of the user, information pertaining to a weight of the userinformation pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

At block 2204, while the user uses an electromechanical machine toperform the first treatment plan for the user, the processing device mayreceive cardio-oncologic data from one or more sensors configured tomeasure the cardio-oncologic data associated with the user. In someembodiments, the cardio-oncologic data may include a weight of the user,a cardiac output of the user, a heartrate of the user, a heart rhythm ofthe user, a blood pressure of the user, a blood oxygen level of theuser, a cardiovascular diagnosis of the user, a non-cardiovasculardiagnosis of the user, a respiration rate of the user, acardio-oncologic diagnosis of the user, an oncologic diagnosis of theuser, a cardio-oncologic diagnosis of the user, a pathological diagnosisrelated to a prostate gland or urogenital tract of the user, spirometrydata related to the user, or some combination thereof.

At block 2206, the processing device may transmit the cardio-oncologicdata. In some embodiments, one or more machine learning models 13 may beexecuted by the server 30 and the machine learning models 13 may be usedto generate a second treatment plan based on the cardio-oncologic data.The second treatment plan may modify at least one exertion level, andthe modification may be based on a standardized measure includingperceived exertion, cardio-oncologic data, and the cardio-oncologichealth issue of the user. In some embodiments, the standardized measureof perceived exertion may include a metabolic equivalent of tasks (MET)or a Borg rating of perceived exertion (RPE).

In some embodiments, the one or more machine learning models generatethe second treatment plan by predicting exercises that will result inthe desired exertion level for each session. The one or more machinelearning models may be trained using data pertaining to the standardizedmeasure of perceived exertion, other users' cardio-oncologic data, andother users' cardio-oncologic health issues as input data, and otherusers' exertion levels that led to desired results as output data. Theinput data and the output data may be labeled and mapped accordingly.

At block 2208, the processing device may receive the second treatmentplan from the server 30. The processing device may implement at least aportion of the treatment plan to cause an operating parameter of theelectromechanical machine to be modified in accordance with the modifiedexertion level set in the second treatment plan. To that end, in someembodiments, the second treatment plan may include a modified parameterpertaining to the electromechanical machine. The modified parameter mayinclude a resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, a velocity, anangular velocity, an acceleration, a torque, or some combinationthereof. The processing device may, based on the modified parameter,control the electromechanical machine.

In some embodiments, transmitting the cardio-oncologic data may includetransmitting the cardio-oncologic data to a second computing device thatrelays the cardio-oncologic data to a third computing device that isassociated with a healthcare professional.

Clauses

Clause 1.7 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user performs a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, at a computing device, a first treatment plan designed        to treat a cardio-oncologic health issue of a user, wherein the        first treatment plan comprises at least two exercise sessions        that, based on the cardio-oncologic health issue of the user,        enable the user to perform an exercise at different exertion        levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive cardio-oncologic data        from one or more sensors configured to measure the        cardio-oncologic data associated with the user;    -   transmit the cardio-oncologic data, wherein one or more machine        learning models are used to generate a second treatment plan;        wherein the second treatment plan modifies at least one exertion        level, and the modification is based on a standardized measure        comprising perceived exertion; the cardio-oncologic data, and        the cardio-oncologic health issue of the user; and    -   receive the second treatment plan.

Clause 2.7 The computer-implemented system of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof, and the computer-implemented system further comprises:

-   -   based on the modified parameter, controlling the        electromechanical machine.

Clause 3.7 The computer-implemented system of any clause herein, whereinthe standardized measure of perceived exertion comprises a metabolicequivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

Clause 4.7 The computer-implemented system of any clause herein, whereinthe one or more machine learning models generate the second treatmentplan by predicting exercises that will result in the desired exertionlevel for each session, and the one or more machine learning models aretrained using data pertaining to the standardized measure of perceivedexertion, other users' cardio-oncologic data, and other users'cardio-oncologic health issues.

Clause 5.7 The computer-implemented system of any clause herein, whereinthe first treatment plan is generated based on attribute data comprisingan eating or drinking schedule of the user, information pertaining to anage of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

Clause 6.7 The computer-implemented system of any clause herein, whereinthe transmitting the cardio-oncologic data further comprisestransmitting the cardio-oncologic data to a second computing device thatrelays the cardio-oncologic data to a third computing device of ahealthcare professional.

Clause 7.7 The computer-implemented system of any clause herein, whereinthe cardio-oncologic data comprises a weight of the user, a cardiacoutput of the user, a heartrate of the user, a heart rhythm of the user,a blood pressure of the user, a blood oxygen level of the user, acardiovascular diagnosis of the user, a non-cardiovascular diagnosis ofthe user, a respiration rate of the user, spirometry data related to theuser, a pulmonary diagnosis of the user, an oncologic diagnosis of theuser, a bariatric diagnosis of the user, a pathological diagnosisrelated to a prostate gland or urogenital tract of the user, or somecombination thereof.

Clause 8.7 A computer-implemented method comprising:

-   -   receiving, at a computing device, a first treatment plan        designed to treat a cardio-oncologic health issue of a user,        wherein the first treatment plan comprises at least two exercise        sessions that, based on the cardio-oncologic health issue of the        user, enable the user to perform an exercise at different        exertion levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receiving cardio-oncologic        data from one or more sensors configured to measure the        cardio-oncologic data associated with the user, wherein the        electromechanical machine is configured to be manipulated by the        user while the user performs the first treatment plan;    -   transmitting the cardio-oncologic data, wherein one or more        machine learning models are used to generate a second treatment        plan; wherein the second treatment plan modifies at least one        exertion level, and the modification is based on a standardized        measure comprising perceived exertion; the cardio-oncologic        data, and the cardio-oncologic health issue of the user; and    -   receiving the second treatment plan.

Clause 9.7 The computer-implemented method of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof, and the computer-implemented system further comprises:

-   -   based on the modified parameter, controlling the        electromechanical machine.

Clause 10.7 The computer-implemented method of any clause herein,wherein the standardized measure of perceived exertion comprises ametabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE).

Clause 11.7 The computer-implemented method of any clause herein,wherein the one or more machine learning models generate the secondtreatment plan by predicting exercises that will result in the desiredexertion level for each session, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' cardio-oncologic data, and other users'cardio-oncologic health issues.

Clause 12.7 The computer-implemented method of any clause herein,wherein the first treatment plan is generated based on attribute datacomprising an eating or drinking schedule of the user, informationpertaining to an age of the user, information pertaining to a sex of theuser, information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

Clause 13.7 The computer-implemented method of any clause herein,wherein the transmitting the cardio-oncologic data further comprisestransmitting the cardio-oncologic data to a second computing device thatrelays the cardio-oncologic data to a third computing device of ahealthcare professional.

Clause 14.7 The computer-implemented method of any clause herein,wherein the cardio-oncologic data comprises a weight of the user, acardiac output of the user, a heartrate of the user, a heart rhythm ofthe user, a blood pressure of the user, a blood oxygen level of theuser, a cardiovascular diagnosis of the user, a non-cardiovasculardiagnosis of the user, a respiration rate of the user, spirometry datarelated to the user, a pulmonary diagnosis of the user, an oncologicdiagnosis of the user, a bariatric diagnosis of the user, a pathologicaldiagnosis related to a prostate gland or urogenital tract of the user,or some combination thereof.

Clause 15.7 A tangible, computer-readable medium storing instructionsthat, when executed, cause a processing device to:

receive, at a computing device, a first treatment plan designed to treata cardio-oncologic health issue of a user, wherein the first treatmentplan comprises at least two exercise sessions that, based on thecardio-oncologic health issue of the user, enable the user to perform anexercise at different exertion levels;

-   -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive cardio-oncologic data        from one or more sensors configured to measure the        cardio-oncologic data associated with the user, wherein the        electromechanical machine is configured to be manipulated by the        user while the user performs the first treatment plan;    -   transmit the cardio-oncologic data, wherein one or more machine        learning models are used to generate a second treatment plan;        wherein the second treatment plan modifies at least one exertion        level, and the modification is based on a standardized measure        comprising perceived exertion; the cardio-oncologic data, and        the cardio-oncologic health issue of the user; and    -   receive the second treatment plan.

Clause 16.7 The computer-readable medium of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, or some combinationthereof, and the computer-implemented system further comprises:

-   -   based on the modified parameter, controlling the        electromechanical machine.

Clause 17.7 The computer-readable medium of any clause herein, whereinthe standardized measure of perceived exertion comprises a metabolicequivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

Clause 18.7 The computer-readable medium of any clause herein, whereinthe one or more machine learning models generate the second treatmentplan by predicting exercises that will result in the desired exertionlevel for each session, and the one or more machine learning models aretrained using data pertaining to the standardized measure of perceivedexertion, other users' cardio-oncologic data, and other users'cardio-oncologic health issues.

Clause 19.7 The computer-readable medium of any clause herein, whereinthe first treatment plan is generated based on attribute data comprisingan eating or drinking schedule of the user, information pertaining to anage of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

Clause 20.7 The computer-readable medium of any clause herein, whereinthe transmitting the cardio-oncologic data further comprisestransmitting the cardio-oncologic data to a second computing device thatrelays the cardio-oncologic data to a third computing device of ahealthcare professional.

System and Method for Identifying Subgroups, Determining CardiacRehabilitation Eligibility, and Prescribing a Treatment Plan for theEligible Subgroups

FIG. 23 generally illustrates an example embodiment of a method 2300 foridentifying subgroups, determining cardiac rehabilitation eligibility,and prescribing a treatment plan for the eligible subgroups according tothe principles of the present disclosure. The method 2300 may beperformed by processing logic that may include hardware (circuitry,dedicated logic, etc.), software, or a combination of both. The method2300 and/or each of their individual functions, subroutines, oroperations may be performed by one or more processing devices of acomputing device (e.g., the computer system 1100 of FIG. 11 )implementing the method 2300. The method 2300 may be implemented ascomputer instructions stored on a memory device and executable by theone or more processing devices. In certain implementations, the method2300 may be performed by a single processing thread. Alternatively, themethod 2300 may be performed by two or more processing threads, eachthread implementing one or more individual functions, routines,subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 2300.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 2300.

At block 2302, a processing device may receive, at a computing device,information pertaining to one or more users. The information may pertainto a cardiac health of the one or more users. In some embodiments, theinformation may be received from an electronic medical records source, athird-party source, or some combination thereof.

At block 2304, the processing device may determine, based on theinformation, a probability associated with the eligibility of the one ormore users for cardiac rehabilitation. In some embodiments, theprobability is either zero or one hundred percent. The cardiacrehabilitation may use an electromechanical machine. In someembodiments, the processing device may determine, based on theinformation, the eligibility of the one or more users for the cardiacrehabilitation using one or more machine learning models trained to mapone or more inputs (e.g., characteristics of the user) to one or moreoutputs (e.g., eligibility of the user for cardiac rehabilitation). Thecardiac rehabilitation may use the electromechanical machine.

At block 2306, responsive to determining that at least one of the one ormore users is eligible for the cardiac rehabilitation, the processingdevice may prescribe a treatment plan to the at least one user. Thetreatment plan may pertain to the cardiac rehabilitation and may includeusage of the electromechanical machine. In some embodiments, thedetermination of eligibility is one of a minimum probability threshold,a condition of eligibility, and/or a condition of non-eligibility. Insome embodiments, the condition may pertain to the one or more usersbeing included in one or more subgroups associated with a geographicregion, having demographic or psychographic characteristics, beingincluded in an underrepresented minority group, being a certain sex,being a certain nationality, having a certain cultural heritage, havinga certain disability, having a certain sexual orientation, havingcertain genotypal or phenotypal characteristics, being a certain gender,having a certain risk level, having certain insurance characteristics,or some combination thereof. In some embodiments, the treatment plan maypertain to cardiac rehabilitation, oncology rehabilitation,rehabilitation from pathologies related to prostate gland or urogenitaltract, pulmonary rehabilitation, bariatric rehabilitation, or somecombination thereof.

In some embodiments, the processing device may generate the treatmentplan using one or more trained machine learning models. The processingdevice may determine, via the one or more machine learning models 13,the treatment plan for the user based on one or more characteristics ofthe user, wherein the one or more characteristics include informationpertaining the user's cardiac health, pulmonary health, oncologichealth, bariatric health, or some combination thereof.

At block 2308, the processing device may assign the electromechanicalmachine to the user to be used to perform the treatment plan pertainingto the cardiac rehabilitation.

In some embodiments, the processing device may determine a number ofusers associated with treatment plans and may determine a geographicregion in which the number of users resides. In some embodiments, basedon the number of users, the processing device may deploy a calculatednumber of electromechanical machines to the geographic region to enablethe users to execute the treatment plans.

Clauses

Clause 1.8 A computer-implemented method comprising:

-   -   receiving, at a computing device, information pertaining to one        or more users, wherein the information pertains to a cardiac        health of the one or more users;    -   determining, based on the information, a probability associated        with the eligibility of one or more users for cardiac        rehabilitation, wherein the cardiac rehabilitation uses an        electromechanical machine;    -   responsive to determining that at least one of the one or more        users is eligible for the cardiac rehabilitation, prescribing a        treatment plan to the at least one user, wherein the treatment        plan pertains to the cardiac rehabilitation and includes usage        of the electromechanical machine and wherein the determination        of eligibility is one of a minimum probability threshold, a        condition of eligibility, and a condition of non-eligibility;        and    -   assigning the electromechanical machine to the user to be used        to perform the treatment plan pertaining to the cardiac        rehabilitation.

Clause 2.8 The computer-implemented method of any clause herein, furthercomprising the condition wherein one or more users are included in oneor more subgroups associated with a geographic region, anunderrepresented minority group, a certain sex, a certain nationality, acertain cultural heritage, a certain disability, a certain sexualpreference, a certain genotype, a certain phenotype, a certain gender, acertain risk level, or some combination thereof.

Clause 3.8 The computer-implemented method of any clause herein, whereinthe information is received from an electronic medical records source, athird-party source, or some combination thereof.

Clause 4.8 The computer-implemented method of any clause herein, furthercomprising:

-   -   determining, via one or more machine learning models, the        treatment plan for the user based on one or more characteristics        of the user, wherein the one or more characteristics comprise        information pertaining the user's cardiac health, pulmonary        health, oncologic health, bariatric health, or some combination        thereof.

Clause 5.8 The computer-implemented method of any clause herein, whereinthe determining, based on the information, of the eligibility of the oneor more users for the cardiac rehabilitation, wherein the cardiacrehabilitation uses the electromechanical machine, and comprises usingone or more trained machine learning models that map one or more inputsto one or more outputs.

Clause 6.8 The computer-implemented method of any clause herein, furthercomprising:

-   -   determining a number of users associated with treatment plans;    -   determining a geographic region in which the number of users        resides; and    -   based on the number of users, deploying a calculated number of        electromechanical machines to the geographic region to enable        the users to execute the treatment plans.

Clause 7.8 The computer-implemented method of any clause herein, whereinthe treatment plan pertains to cardiac rehabilitation, oncologyrehabilitation, rehabilitation from pathologies related to the prostategland or urogenital tract, pulmonary rehabilitation, bariatricrehabilitation, or some combination thereof.

Clause 8.8 The computer-implemented method of any clause herein, whereinthe probability is either zero or one hundred percent.

Clause 9.8 A computer-implemented system comprising:

-   -   a memory device storing instructions; and    -   a processing device communicatively coupled to the memory        device, wherein the processing device executes the instructions        to:    -   receive, at a computing device, information pertaining to one or        more users, wherein the information pertains to a cardiac health        of the one or more users;    -   determine, based on the information, a probability associated        with the eligibility of one or more users for cardiac        rehabilitation, wherein the cardiac rehabilitation uses an        electromechanical machine;    -   responsive to determining that at least one of the one or more        users is eligible for the cardiac rehabilitation, prescribe a        treatment plan to the at least one user, wherein the treatment        plan pertains to the cardiac rehabilitation and includes usage        of the electromechanical machine and wherein the determination        of eligibility is one of a minimum probability threshold, a        condition of eligibility, and a condition of non-eligibility;        and    -   assign the electromechanical machine to the user to be used to        perform the treatment plan pertaining to the cardiac        rehabilitation.

Clause 10.8 The computer-implemented system of any clause herein,wherein one or more users are included in one or more subgroupsassociated with a geographic region, an underrepresented minority group,a certain sex, a certain nationality, a certain cultural heritage, acertain disability, a certain sexual preference, a certain genotype, acertain phenotype, a certain gender, a certain risk level, or somecombination thereof.

Clause 11.8 The computer-implemented system of any clause herein,wherein the information is received from an electronic medical recordssource, a third-party source, or some combination thereof.

Clause 12.8 The computer-implemented system of any clause herein,wherein the processing device is to:

determine, via one or more machine learning models, the treatment planfor the user based on one or more characteristics of the user, whereinthe one or more characteristics comprise information pertaining theuser's cardiac health, pulmonary health, oncologic health, bariatrichealth, or some combination thereof.

Clause 13.8 The computer-implemented system of any clause herein,wherein the determining, based on the information, of the eligibility ofthe one or more users for the cardiac rehabilitation, wherein thecardiac rehabilitation uses the electromechanical machine, and comprisesusing one or more trained machine learning models that map one or moreinputs to one or more outputs.

Clause 14.8 The computer-implemented system of any clause herein,wherein the processing device is to:

-   -   determine a number of users associated with treatment plans;    -   determine a geographic region in which the number of users        resides; and    -   based on the number of users, deploy a calculated number of        electromechanical machines to the geographic region to enable        the users to execute the treatment plans.

Clause 15.8 The computer-implemented system of any clause herein,wherein the treatment plan pertains to cardiac rehabilitation, oncologyrehabilitation, rehabilitation from pathologies related to the prostategland or urogenital tract, pulmonary rehabilitation, bariatricrehabilitation, or some combination thereof.

Clause 16.8 The computer-implemented system of any clause herein,wherein the probability is either zero or one hundred percent.

Clause 17.8 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive, at a computing device, information pertaining to one or        more users, wherein the information pertains to a cardiac health        of the one or more users;    -   determine, based on the information, a probability associated        with the eligibility of one or more users for cardiac        rehabilitation, wherein the cardiac rehabilitation uses an        electromechanical machine;    -   responsive to determining that at least one of the one or more        users is eligible for the cardiac rehabilitation, prescribe a        treatment plan to the at least one user, wherein the treatment        plan pertains to the cardiac rehabilitation and includes usage        of the electromechanical machine and wherein the determination        of eligibility is one of a minimum probability threshold, a        condition of eligibility, and a condition of non-eligibility;        and    -   assign the electromechanical machine to the user to be used to        perform the treatment plan pertaining to the cardiac        rehabilitation.

Clause 18.8 The computer-readable medium of any clause herein, whereinone or more users are included in one or more subgroups associated witha geographic region, an underrepresented minority group, a certain sex,a certain nationality, a certain cultural heritage, a certaindisability, a certain sexual preference, a certain genotype, a certainphenotype, a certain gender, a certain risk level, or some combinationthereof.

Clause 19.8 The computer-readable medium of any clause herein, whereinthe information is received from an electronic medical records source, athird-party source, or some combination thereof.

Clause 20.8 The computer-readable medium of any clause herein, whereinthe processing device is to:

-   -   determine, via one or more machine learning models, the        treatment plan for the user based on one or more characteristics        of the user, wherein the one or more characteristics comprise        information pertaining the user's cardiac health, pulmonary        health, oncologic health, bariatric health, or some combination        thereof.

System and Method for Using AI/ML to Provide an Enhanced User InterfacePresenting Data Pertaining to Cardiac Health, Bariatric Health,Pulmonary Health, and/or Cardio-Oncologic Health for the Purpose ofPerforming Preventative Actions

FIG. 24 generally illustrates an example embodiment of a method 2400 forusing artificial intelligence and machine learning to provide anenhanced user interface presenting data pertaining to cardiac health,bariatric health, pulmonary health, and/or cardio-oncologic health forthe purpose of performing preventative actions according to theprinciples of the present disclosure. The method 2400 may be performedby processing logic that may include hardware (circuitry, dedicatedlogic, etc.), software, or a combination of both. The method 2400 and/oreach of their individual functions, subroutines, or operations may beperformed by one or more processing devices of a computing device (e.g.,the computer system 1100 of FIG. 11 ) implementing the method 2400. Themethod 2400 may be implemented as computer instructions stored on amemory device and executable by the one or more processing devices. Incertain implementations, the method 2400 may be performed by a singleprocessing thread. Alternatively, the method 2400 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods.

In some embodiments, a system may be used to implement the method 2400.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 2400.

At block 2402, the processing device may receive, at a computing device,one or more characteristics associated with the user. The one or morecharacteristics may include personal information, performanceinformation, measurement information, cohort information, familialinformation, healthcare professional information, or some combinationthereof.

At block 2404, the processing device may determine, based on the one ormore characteristics, one or more conditions of the user. The one ormore conditions may pertain to cardiac health, pulmonary health,bariatric health, oncologic health, or some combination thereof.

At block 2406, based on the one or more conditions, the processingdevice may identify, using one or more trained machine learning models,one or more subgroups representing different partitions of the one ormore characteristics to present via the display.

At block 2408, the processing device may present, via the display, theone or more subgroups. In some embodiments, the processing device maypresent one or more graphical elements associated with the one or moresubgroups. The one or more graphical elements may be arranged based on apriority, a severity, or both of the one or more subgroups. The one ormore graphical elements may include at least one input mechanism thatenables performing a preventative action. The one or more preventativeactions may include modifying an operating parameter of theelectromechanical machine, initiating a telecommunications transmission,contacting a computing device associated with the user, or somecombination thereof.

In some embodiments, the processing device may contact a secondcomputing device of a healthcare professional if a portion of the one ormore subgroups is presented on the display for a threshold period oftime, if the portion of the one or more subgroups exceeds a thresholdlevel, or both.

In some embodiments, the processing device may verify an identity of ahealthcare professional prior to presenting the one or more subgroups onthe display. The verifying the identity of the healthcare professionalmay include verifying biometric data associated with the healthcareprofessional, two-factor authentication (2FA) methods used by thehealthcare professional, credential authentication of the healthcareprofessional, or other authentical methods consistent with regulatoryrequirements.

Clauses

Clause 1.9 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user performs a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the user, treatment plan, or both; and    -   a processing device configured to:    -   receive, at a computing device, one or more characteristics        associated with the user, wherein the one or more        characteristics comprise personal information, performance        information, measurement information, cohort information,        familial information, healthcare professional information, or        some combination thereof;    -   determine, based on the one or more characteristics, one or more        conditions of the user, wherein the one or more conditions        pertain to cardiac health, pulmonary health, bariatric health,        oncologic health, or some combination thereof;    -   based on the one or more conditions, identify, using one or more        trained machine learning models, one or more subgroups        representing different partitions of the one or more        characteristics to present via the display; and    -   present, via the display, the one or more subgroups.

Clause 2.9 The computer-implemented system of any clause herein, whereinthe processing device is further to present one or more graphicalelements associated with the one or more subgroups.

Clause 3.9 The computer-implemented system of any clause herein, whereinthe one or more graphical elements are arranged based on a priority, aseverity, or both of the one or more subgroups.

Clause 4.9 The computer-implemented system of any clause herein, whereinthe one or more graphical elements comprise at least one input mechanismthat enables performing a preventative action.

Clause 5.9 The computer-implemented system of any clause herein, whereinthe preventative action comprises modifying an operating parameter ofthe electromechanical machine, initiating a telecommunicationstransmission, contacting a computing device associated with the user, orsome combination thereof.

Clause 6.9 The computer-implemented system of any clause herein, whereinthe processing device is further to contact a second computing device ofa healthcare professional if a portion of the one or more subgroups ispresented on the display for a threshold period of time, if the portionof the one or more subgroups exceeds a threshold level, or both.

Clause 7.9 The computer-implemented system of any clause herein, whereinthe processing device is configured to verify an identity of ahealthcare professional prior to presenting the one or more subgroups onthe display, wherein verifying the identity of the healthcareprofessional comprises verifying biometric data associated with thehealthcare professional, two-factor authentication (2FA) methods used bythe healthcare professional, or other authentical methods consistentwith regulatory requirements.

Clause 8.9 A computer-implemented method comprising:

-   -   receiving, at a computing device, one or more characteristics        associated with the user, wherein the one or more        characteristics comprise personal information, performance        information, measurement information, cohort information,        familial information, healthcare professional information, or        some combination thereof;    -   determining, based on the one or more characteristics, one or        more conditions of the user, wherein the one or more conditions        pertain to cardiac health, pulmonary health, bariatric health,        oncologic health, or some combination thereof;    -   based on the one or more conditions, identifying, using one or        more trained machine learning models, one or more subgroups        representing different partitions of the one or more        characteristics to present via a display; and    -   presenting, via the display, the one or more subgroups.

Clause 9.9 The computer-implemented method of any clause herein, furthercomprising presenting one or more graphical elements associated with theone or more subgroups.

Clause 10.9 The computer-implemented method of any clause herein,wherein the one or more graphical elements are arranged based on apriority, a severity, or both of the one or more subgroups.

Clause 11.9 The computer-implemented method of any clause herein,wherein the one or more graphical elements comprise at least one inputmechanism that enables performing a preventative action.

Clause 12.9 The computer-implemented method of any clause herein,wherein the preventative action comprises modifying an operatingparameter of the electromechanical machine, contacting an emergencyservice, contacting a computing device associated with the user, or somecombination thereof.

Clause 13.9 The computer-implemented method of any clause herein,further comprising contacting a second computing device of a healthcareprofessional if a portion of the one or more subgroups is presented onthe display for a threshold period of time, if the portion of the one ormore subgroups exceeds a threshold level, or both.

Clause 14.9 The computer-implemented method of any clause herein,wherein the processing device is configured to verify an identity of ahealthcare professional prior to presenting the one or more subgroups onthe display, wherein verifying the identity of the healthcareprofessional comprises verifying biometric data associated with thehealthcare professional, two-factor authentication (2FA) methods used bythe healthcare professional, or other authentical methods consistentwith regulatory requirements.

Clause 15.9 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive one or more characteristics associated with the user,        wherein the one or more characteristics comprise personal        information, performance information, measurement information,        cohort information, familial information, healthcare        professional information, or some combination thereof;    -   determine, based on the one or more characteristics, one or more        conditions of the user, wherein the one or more conditions        pertain to cardiac health, pulmonary health, bariatric health,        oncologic health, or some combination thereof;    -   based on the one or more conditions, identify, using one or more        trained machine learning models, one or more subgroups        representing different partitions of the one or more        characteristics to present via a display; and    -   present, via the display, the one or more subgroups.

Clause 16.9 The computer-readable medium of any clause herein, whereinthe processing device is to present one or more graphical elementsassociated with the one or more subgroups.

Clause 17.9 The computer-readable medium of any clause herein, whereinthe one or more graphical elements are arranged based on a priority, aseverity, or both of the one or more subgroups.

Clause 18.9 The computer-readable medium of any clause herein, whereinthe one or more graphical elements comprise at least one input mechanismthat enables performing a preventative action.

Clause 19.9 The computer-readable medium of any clause herein, whereinthe preventative action comprises modifying an operating parameter ofthe electromechanical machine, contacting an emergency service,contacting a computing device associated with the user, or somecombination thereof.

Clause 20.9 The computer-readable medium of any clause herein, furthercomprising contacting a second computing device of a healthcareprofessional if a portion of the one or more subgroups is presented onthe display for a threshold period of time, if the portion of the one ormore subgroups exceeds a threshold level, or both.

System and Method for Using AI/ML and Telemedicine for Long-Term CareVia an Electromechanical Machine

FIG. 25 generally illustrates an example embodiment of a method 2500 forusing artificial intelligence and machine learning and telemedicine forlong-term care via an electromechanical machine according to theprinciples of the present disclosure. The method 2500 may be performedby processing logic that may include hardware (circuitry, dedicatedlogic, etc.), software, or a combination of both. The method 2500 and/oreach of their individual functions, subroutines, or operations may beperformed by one or more processing devices of a computing device (e.g.,the computer system 1100 of FIG. 11 ) implementing the method 2500. Themethod 2500 may be implemented as computer instructions stored on amemory device and executable by the one or more processing devices. Incertain implementations, the method 2500 may be performed by a singleprocessing thread. Alternatively, the method 2500 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods.

In some embodiments, a system may be used to implement the method 2500.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 2500.

At block 2502, the processing device may receive, at a computing device,a first treatment plan designed to treat a long-term care health issueof a user. The first treatment plan may include at least two exercisesessions that, based on the long-term care health issue of the user,enable the user to perform an exercise at different exertion levels. Insome embodiments, information pertaining to the user's long-term carehealth issue may be received from an application programming interfaceassociated with an electronic medical records system.

In some embodiments, the first treatment plan may be generated based onattribute data including an eating or drinking schedule of the user,information pertaining to an age of the user, information pertaining toa sex of the user, information pertaining to a weight of the userinformation pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

At block 2504, while the user uses an electromechanical machine toperform the first treatment plan for the user, the processing device mayreceive data from one or more sensors configured to measure the dataassociated with the long-term care health issue of the user. In someembodiments, the data may include a procedure performed on the user, anelectronic medical record associated with the user, a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a respiration rate of theuser, spirometry data related to the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a bariatric diagnosis of theuser, a pathological diagnosis related to a prostate gland or urogenitaltract of the user, or some combination thereof.

At block 2506, the processing device may transmit the data. In someembodiments, one or more machine learning models 13 may be executed bythe server 30 and the machine learning models 13 may be used to generatea second treatment plan based on the data and/or the long-term carehealth issues of users. The second treatment plan may modify at leastone exertion level, and the modification may be based on a standardizedmeasure including perceived exertion, the data, and the long-term carehealth issue of the user. In some embodiments, the standardized measureof perceived exertion may include a metabolic equivalent of tasks (MET)or a Borg rating of perceived exertion (RPE).

In some embodiments, the one or more machine learning models generatethe second treatment plan by predicting exercises that will result inthe desired exertion level for each session. The one or more machinelearning models may be trained using data pertaining to the standardizedmeasure of perceived exertion, other users' data, and other users'long-term care health issues as input data, and other users' exertionlevels that led to desired results as output data. The input data andthe output data may be labeled and mapped accordingly.

At block 2508, the processing device may receive the second treatmentplan from the server 30. The processing device may implement at least aportion of the treatment plan to cause an operating parameter of theelectromechanical machine to be modified in accordance with the modifiedexertion level set in the second treatment plan. To that end, in someembodiments, the second treatment plan may include a modified parameterpertaining to the electromechanical machine. The modified parameter mayinclude a resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, a velocity, anangular velocity, an acceleration, a torque, or some combinationthereof. The processing device may, based on the modified parameter,control the electromechanical machine.

In some embodiments, transmitting the data may include transmitting thedata to a second computing device that relays the long-term care healthissue data to a third computing device that is associated with ahealthcare professional.

Clauses

Clause 1.10 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while performing a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, at a computing device, a first treatment plan designed        to treat a long-term care health issue of a user, wherein the        first treatment plan comprises at least two exercise sessions        that, based on the long-term care health issue of the user,        enable the user to perform one or more exercises at respectively        different exertion levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive data from one or more        sensors configured to measure the data associated with the        long-term care health issue of the user;    -   transmit the data, wherein one or more machine learning models        are used to generate a second treatment plan, wherein the second        treatment plan modifies at least one exertion level, and the        modification is based on a standardized measure comprising        perceived exertion, the data, and the long-term care health        issue of the user; and    -   receive the second treatment plan.

Clause 2.10 The computer-implemented system of any clause herein,wherein the second treatment plan comprises a modified parameterpertaining to the electromechanical machine, wherein the modifiedparameter comprises a resistance, a range of motion, a length of time,an angle of a component of the electromechanical machine, a speed, avelocity, an angular velocity, an acceleration, a torque, or somecombination thereof, and the computer-implemented system furthercomprises:

-   -   controlling the electromechanical machine based on the modified        parameter.

Clause 3.10 The computer-implemented system of any clause herein,wherein the standardized measure of perceived exertion comprises ametabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE).

Clause 4.10 The computer-implemented system of any clause herein,wherein the one or more machine learning models generate the secondtreatment plan by predicting exercises that will result in the desiredexertion level for each session, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' data, and other users' long-term carehealth issues.

Clause 5.10 The computer-implemented system of any clause herein,wherein the first treatment plan is generated based on attribute datacomprising an eating or drinking schedule of the user, informationpertaining to an age of the user, information pertaining to a sex of theuser, information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed,information pertaining to long term care health issues of other users,or some combination thereof.

Clause 6.10 The computer-implemented system of any clause herein,wherein the transmitting the data further comprises transmitting thedata to a second computing device that relays the data to a thirdcomputing device of a healthcare professional.

Clause 7.10 The computer-implemented system of any clause herein,wherein the data comprises a procedure performed on the user, anelectronic medical record associated with the user, a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a respiration rate of theuser, spirometry data related to the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a bariatric diagnosis of theuser, a pathological diagnosis related to a prostate gland or urogenitaltract of the user, or some combination thereof.

Clause 8.10 A computer-implemented method comprising:

-   -   receiving, at a computing device, a first treatment plan        designed to treat a long-term care health issue of a user,        wherein the first treatment plan comprises at least two exercise        sessions that, based on the long-term care health issue of the        user, enable the user to perform one or more exercises at        respectively different exertion levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receiving data from one or        more sensors configured to measure the data associated with the        long-term care health issue of the user, wherein the        electromechanical machine is configured to be manipulated by the        user while performing the first treatment plan;    -   transmitting the data, wherein one or more machine learning        models are used to generate a second treatment plan, wherein the        second treatment plan modifies at least one exertion level, and        the modification is based on a standardized measure comprising        perceived exertion, the data, and the long-term care health        issue of the user; and    -   receiving the second treatment plan.

Clause 9.10 The computer-implemented method of any clause herein,wherein the second treatment plan comprises a modified parameterpertaining to the electromechanical machine, wherein the modifiedparameter comprises a resistance, a range of motion, a length of time,an angle of a component of the electromechanical machine, a speed, avelocity, an angular velocity, an acceleration, a torque, or somecombination thereof, and the computer-implemented system furthercomprises:

-   -   controlling the electromechanical machine based on the modified        parameter.

Clause 10.10 The computer-implemented method of any clause herein,wherein the standardized measure of perceived exertion comprises ametabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE).

Clause 11.10 The computer-implemented method of any clause herein,wherein the one or more machine learning models generate the secondtreatment plan by predicting exercises that will result in the desiredexertion level for each session, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' data, and other users' long-term carehealth issues.

Clause 12.10 The computer-implemented method of any clause herein,wherein the first treatment plan is generated based on attribute datacomprising an eating or drinking schedule of the user, informationpertaining to an age of the user, information pertaining to a sex of theuser, information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed,information pertaining to long term care health issues of other users,or some combination thereof.

Clause 13.10 The computer-implemented method of any clause herein,wherein the transmitting the data further comprises transmitting thedata to a second computing device that relays the data to a thirdcomputing device of a healthcare professional.

Clause 14.10 The computer-implemented method of any clause herein,wherein the data comprises a procedure performed on the user, anelectronic medical record associated with the user, a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a respiration rate of theuser, spirometry data related to the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a bariatric diagnosis of theuser, a pathological diagnosis related to a prostate gland or urogenitaltract of the user, or some combination thereof.

Clause 15.10 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive a first treatment plan designed to treat a long-term        care health issue of a user, wherein the first treatment plan        comprises at least two exercise sessions that, based on the        long-term care health issue of the user, enable the user to        perform one or more exercises at respectively different exertion        levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive data from one or more        sensors configured to measure the data associated with the        long-term care health issue of the user, wherein the        electromechanical machine is configured to be manipulated by the        user while performing the first treatment plan;    -   transmit the data, wherein one or more machine learning models        are used to generate a second treatment plan, wherein the second        treatment plan modifies at least one exertion level, and the        modification is based on a standardized measure comprising        perceived exertion, the data, and the long-term care health        issue of the user; and    -   receive the second treatment plan.

Clause 16.10 The computer-readable medium of any clause herein, whereinthe second treatment plan comprises a modified parameter pertaining tothe electromechanical machine, wherein the modified parameter comprisesa resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, a velocity, anangular velocity, an acceleration, a torque, or some combinationthereof, and the computer-implemented system further comprises:

-   -   controlling the electromechanical machine based on the modified        parameter.

Clause 17. The computer-readable medium of any clause herein, whereinthe standardized measure of perceived exertion comprises a metabolicequivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

Clause 18.10 The computer-readable medium of any clause herein, whereinthe one or more machine learning models generate the second treatmentplan by predicting exercises that will result in the desired exertionlevel for each session, and the one or more machine learning models aretrained using data pertaining to the standardized measure of perceivedexertion, other users' data, and other users' long-term care healthissues.

Clause 19.10 The computer-readable medium of any clause herein, whereinthe first treatment plan is generated based on attribute data comprisingan eating or drinking schedule of the user, information pertaining to anage of the user, information pertaining to a sex of the user,information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed,information pertaining to long term care health issues of other users,or some combination thereof.

Clause 20.10 The computer-readable medium of any clause herein, whereinthe transmitting the data further comprises transmitting the data to asecond computing device that relays the data to a third computing deviceof a healthcare professional.

System and Method for Assigning Users to be Monitored by Observers,Where the Assignment and Monitoring are Based on Promulgated Regulations

FIG. 26 generally illustrates an example embodiment of a method 2600 forassigning users to be monitored by observers where the assignment andmonitoring are based on promulgated regulations according to theprinciples of the present disclosure. The method 2600 may be performedby processing logic that may include hardware (circuitry, dedicatedlogic, etc.), software, or a combination of both. The method 2600 and/oreach of their individual functions, subroutines, or operations may beperformed by one or more processing devices of a computing device (e.g.,the computer system 1100 of FIG. 11 ) implementing the method 2600. Themethod 2600 may be implemented as computer instructions stored on amemory device and executable by the one or more processing devices. Incertain implementations, the method 2600 may be performed by a singleprocessing thread. Alternatively, the method 2600 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods.

In some embodiments, a system may be used to implement the method 2600.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 2600.

At block 2602, the processing device may receive, at a computing device,one or more requests to initiate one or more monitored sessions of theone or more users performing the one or more treatment plans. Thecomputing device may be associated with a healthcare professional. Thedisplay of the interface may be presented on the computing device andthe interface may enable the healthcare professional to privatelycommunicate with each user being monitored in real-time or nearreal-time while performing the one or more treatment plans. In someembodiments, the one or more treatment plans may pertain to cardiacrehabilitation, pulmonary rehabilitation, bariatric rehabilitation,cardio-oncology rehabilitation, or some combination thereof.

At block 2604, the processing device may determine, based one or morerules, whether the computing device is currently monitoring a thresholdnumber of sessions. The one or more rules may include a governmentagency regulation, a law, a protocol, or some combination thereof. Forexample, an FDA regulation may specify that up to 5 patients may beobserved by 1 healthcare professional at any given time. That is, ahealthcare professional may not concurrently or simultaneously observemore than 5 patients at any given moment in time.

At block 2606, responsive to determining that the computing device isnot currently monitoring the threshold number of sessions, theprocessing device may initiate via the computing device at least one ofthe one or more monitored sessions.

In some embodiments, responsive to determining the computing device iscurrently monitoring the threshold number of sessions, the processingdevice may identify a second computing device. The second computingdevice may be associated with a second healthcare professional that islocated proximate (e.g., a physician working for the same practice asthe healthcare professional) or remote (e.g., a physician located inanother city or state or country). The processing device may determine,based on the one or more rules, whether the second computing device iscurrently monitoring the threshold number of sessions. Responsive todetermining the second computing device is not currently monitoring thethreshold number of sessions, the processing device may initiate atleast one of the one or more monitored sessions via the second computingdevice.

Further, in some embodiments, when the computing device is monitoringthe threshold number of sessions, the processing device may identify asecond computing device, and the identification may be performed withoutconsidering a geographical location of the second computing devicerelative to a geographical location of the electromechanical machine.

In some embodiments, the processing device may use one or more machinelearning models trained to determine a prioritized order of users toinitiate a monitored session. The one or more machine learning modelsmay be trained to determine the priority based on one or morecharacteristics of the one or more users. For example, if a user has afamilial history of cardiac disease or other similar life threateningdisease, that user may be given a higher priority for a monitoredsession than a user that does not have that familial history.Accordingly, in some embodiments, the most at risk users in terms ofhealth are given priority to engage in monitored sessions withhealthcare professionals during their rehabilitation. In someembodiments, the prioritization may be adjusted based on other factors,such as compensation. For example, if a user desires to receiveprioritized treatment, they may pay a certain amount of money to beadvanced in priority for monitored sessions during their rehabilitation.

Clauses

Clause 1.11 A computer-implemented system, comprising:

-   -   one or more electromechanical machines configured to be        manipulated by one or more users while the users are performing        one or more treatment plans;        -   an interface comprising a display configured to present            information pertaining to the treatment plan; and        -   a processing device configured to:    -   receive, at a computing device, one or more requests to initiate        one or more monitored sessions of the one or more users        performing the one or more treatment plans;    -   determine, based on one or more rules, whether the computing        device is currently monitoring a threshold number of sessions;        and    -   responsive to determining that the computing device is not        currently monitoring the threshold number of sessions, initiate        via the computing device at least one of the one or more        monitored sessions.

Clause 2.11 The computer-implemented system of any clause herein,wherein the processing device is further configured to:

-   -   responsive to determining the computing device is currently        monitoring the threshold number of sessions, identify a second        computing device;    -   determine, based on the one or more rules, whether the second        computing device is currently monitoring the threshold number of        sessions; and    -   responsive to determining the second computing device is not        currently monitoring the threshold number of sessions, initiate        at least one of the one or more monitored sessions via the        second computing device.

Clause 3.11 The computer-implemented system of any clause herein,wherein the one or more rules comprise a government agency regulation, alaw, a protocol, or some combination thereof.

Clause 4.11 The computer-implemented system of any clause herein,wherein the computing device is associated with a healthcareprofessional, and the interface enables the healthcare professional toprivately communicate with each user being monitored in real-time ornear real-time while performing the one or more treatment plans.

Clause 5.11 The computer-implemented system of any clause herein,wherein the one or more treatment plans pertain to cardiacrehabilitation, pulmonary rehabilitation, bariatric rehabilitation,cardio-oncology rehabilitation, or some combination thereof.

Clause 6.11 The computer-implemented system of any clause herein,wherein, when the computing device is monitoring the threshold number ofsessions, the processing device is further configured to identify asecond computing device, and wherein the identification is performedwithout considering a geographical location of the second computingdevice relative to a geographical location the electromechanicalmachine.

Clause 7.11 The computer-implemented system of any clause herein,wherein the processing device is further configured to use one or moremachine learning models to determine a prioritized order of users toinitiate a monitored session, and the one or more machine learningmodels are trained to determine the priority based on one or morecharacteristics of the one or more users.

Clause 8.11 A computer-implemented method comprising:

-   -   receiving, at a computing device, one or more requests to        initiate one or more monitored sessions of the one or more users        performing the one or more treatment plans;    -   determining, based on one or more rules, whether the computing        device is currently monitoring a threshold number of sessions;        and    -   responsive to determining that the computing device is not        currently monitoring the threshold number of sessions,        initiating via the computing device at least one of the one or        more monitored sessions.

Clause 9.11 The computer-implemented method of any clause herein,further comprising:

-   -   responsive to determining the computing device is currently        monitoring the threshold number of sessions, identifying a        second computing device;    -   determining, based on the one or more rules, whether the second        computing device is currently monitoring the threshold number of        sessions; and    -   responsive to determining the second computing device is not        currently monitoring the threshold number of sessions,        initiating at least one of the one or more monitored sessions        via the second computing device.

Clause 10.11 The computer-implemented method of any clause herein,wherein the one or more rules comprise a government agency regulation, alaw, a protocol, or some combination thereof.

Clause 11.11 The computer-implemented method of any clause herein,wherein the computing device is associated with a healthcareprofessional, and the interface enables the healthcare professional toprivately communicate with each user being monitored in real-time ornear real-time while performing the one or more treatment plans.

Clause 12.11 The computer-implemented method of any clause herein,wherein the one or more treatment plans pertain to cardiacrehabilitation, pulmonary rehabilitation, bariatric rehabilitation,cardio-oncology rehabilitation, or some combination thereof.

Clause 13.11 The computer-implemented method of any clause herein,wherein, when the computing device is monitoring the threshold number ofsessions, the processing device is further configured to identify asecond computing device, and wherein the identification is performedwithout considering a geographical location of the second computingdevice relative to a geographical location the electromechanicalmachine.

Clause 14.11 The computer-implemented method of any clause herein,further comprising using one or more machine learning models todetermine a prioritized order of users to initiate a monitored session,and the one or more machine learning models are trained to determine thepriority based on one or more characteristics of the one or more users.

Clause 15.11 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive, at a computing device, one or more requests to initiate        one or more monitored sessions of the one or more users        performing the one or more treatment plans;    -   determine, based on one or more rules, whether the computing        device is currently monitoring a threshold number of sessions;        and    -   responsive to determining that the computing device is not        currently monitoring the threshold number of sessions, initiate        via the computing device at least one of the one or more        monitored sessions.

Clause 16.11 The computer-readable medium of any clause herein, whereinthe processing device is to:

-   -   responsive to determining the computing device is currently        monitoring the threshold number of sessions, identify a second        computing device;    -   determine, based on the one or more rules, whether the second        computing device is currently monitoring the threshold number of        sessions; and    -   responsive to determining the second computing device is not        currently monitoring the threshold number of sessions, initiate        at least one of the one or more monitored sessions via the        second computing device.

Clause 17.11 The computer-readable medium of any clause herein, whereinthe one or more rules comprise a government agency regulation, a law, aprotocol, or some combination thereof.

Clause 18.11 The computer-readable medium of any clause herein, whereinthe computing device is associated with a healthcare professional, andthe interface enables the healthcare professional to privatelycommunicate with each user being monitored in real-time or nearreal-time while performing the one or more treatment plans.

Clause 19.11 The computer-readable medium of any clause herein, whereinthe one or more treatment plans pertain to cardiac rehabilitation,pulmonary rehabilitation, bariatric rehabilitation, cardio-oncologyrehabilitation, or some combination thereof.

Clause 20.11 The computer-readable medium of any clause herein, wherein,when the computing device is monitoring the threshold number ofsessions, the processing device is further configured to identify asecond computing device, and wherein the identification is performedwithout considering a geographical location of the second computingdevice relative to a geographical location the electromechanicalmachine.

System and Method for Using AI/ML and Telemedicine for Cardiac andPulmonary Treatment Via an Electromechanical Machine of SexualPerformance

FIG. 27 generally illustrates an example embodiment of a method 2700 forusing artificial intelligence and machine learning and telemedicine forcardiac and pulmonary treatment via an electromechanical machine ofsexual performance according to the principles of the presentdisclosure. The method 2700 may be performed by processing logic thatmay include hardware (circuitry, dedicated logic, etc.), software, or acombination of both. The method 2700 and/or each of their individualfunctions, subroutines, or operations may be performed by one or moreprocessing devices of a computing device (e.g., the computer system 1100of FIG. 11 ) implementing the method 2700. The method 2700 may beimplemented as computer instructions stored on a memory device andexecutable by the one or more processing devices. In certainimplementations, the method 2700 may be performed by a single processingthread. Alternatively, the method 2700 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 2700.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 2700.

At block 2702, the processing device may receive, at a computing device,a first treatment plan designed to treat a sexual performance healthissue of a user. The first treatment plan may include at least twoexercise sessions that, based on the sexual performance health issue ofthe user, enable the user to perform an exercise at different exertionlevels. In some embodiments, sexual performance information pertainingto the user may be received from an application programming interfaceassociated with an electronic medical records system. In someembodiments, the sexual performance health issue of the user may includeerectile dysfunction, abnormally low or high testosterone levels,abnormally low or high estrogen levels, abnormally low or high progestinlevels, diminished libido, health conditions associated with abnormallevels of any of the foregoing hormones or of other hormones, or somecombination thereof.

In some embodiments, the first treatment plan may be generated based onattribute data including an eating or drinking schedule of the user,information pertaining to an age of the user, information pertaining toa sex of the user, information pertaining to a weight of the userinformation pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

At block 2704, while the user uses an electromechanical machine toperform the first treatment plan for the user, the processing device mayreceive data from one or more sensors configured to measure the dataassociated with the sexual performance health issue of the user. In someembodiments, the data may include a procedure performed on the user, anelectronic medical record associated with the user, a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a respiration rate of theuser, spirometry data related to the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a bariatric diagnosis of theuser, a pathological diagnosis related to a prostate gland or urogenitaltract of the user, or some combination thereof.

At block 2706, the processing device may transmit the data. In someembodiments, one or more machine learning models 13 may be executed bythe server 30 and the machine learning models 13 may be used to generatea second treatment plan based on the data and/or the sexual performancehealth issues of users. The second treatment plan may modify at leastone exertion level, and the modification may be based on a standardizedmeasure including perceived exertion, the data, and the sexualperformance health issue of the user. In some embodiments, thestandardized measure of perceived exertion may include a metabolicequivalent of tasks (MET) or a Borg rating of perceived exertion (RPE).

In some embodiments, the one or more machine learning models generatethe second treatment plan by predicting exercises that will result inthe desired exertion level for each session. The one or more machinelearning models may be trained using data pertaining to the standardizedmeasure of perceived exertion, other users' data, and other users'sexual performance health issues as input data, and other users'exertion levels that led to desired results as output data. The inputdata and the output data may be labeled and mapped accordingly.

At block 2708, the processing device may receive the second treatmentplan from the server 30. The processing device may implement at least aportion of the treatment plan to cause an operating parameter of theelectromechanical machine to be modified in accordance with the modifiedexertion level set in the second treatment plan. To that end, in someembodiments, the second treatment plan may include a modified parameterpertaining to the electromechanical machine. The modified parameter mayinclude a resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, a velocity, anangular velocity, an acceleration, a torque, or some combinationthereof. The processing device may, based on the modified parameter,control the electromechanical machine.

In some embodiments, transmitting the data may include transmitting thedata to a second computing device that relays the sexual performancehealth issue data to a third computing device that is associated with ahealthcare professional.

Clauses

Clause 1.12 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user performs a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, at a computing device, a first treatment plan designed        to treat a sexual performance health issue of a user, wherein        the first treatment plan comprises at least two exercise        sessions that, based on the sexual performance health issue of        the user, enable the user to perform an exercise at different        exertion levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive data from one or more        sensors configured to measure the data associated with the        sexual performance health issue of the user;    -   transmit the data, wherein one or more machine learning models        are used to generate a second treatment plan, wherein the second        treatment plan modifies at least one exertion level, and the        modification is based on a standardized measure comprising        perceived exertion, the data, and the sexual performance health        issue of the user; and    -   receive the second treatment plan.

Clause 2.12 The computer-implemented system of any clause herein,wherein the data comprises information pertaining to cardiac health ofthe user, oncologic health of the user, pulmonary health of the user,bariatric health of the user, rehabilitation from pathologies related toa prostate gland or urogenital tract, or some combination thereof.

Clause 3.12 The computer-implemented system of any clause herein,wherein the second treatment plan comprises a modified parameterpertaining to the electromechanical machine, wherein the modifiedparameter comprises a resistance, a range of motion, a length of time,an angle of a component of the electromechanical machine, a speed, avelocity, an angular velocity, an acceleration, a torque, or somecombination thereof or some combination thereof, and thecomputer-implemented system further comprises:

-   -   controlling the electromechanical machine based on the modified        parameter.

Clause 4.12 The computer-implemented system of any clause herein,wherein the standardized measure of perceived exertion comprises ametabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE).

Clause 5.12 The computer-implemented system of any clause herein,wherein, by predicting exercises that will result in the desiredexertion level for each session, the one or more machine learning modelsgenerate the second treatment plan, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' data, and other users' sexualperformance health issues.

Clause 6.12 The computer-implemented system of any clause herein,wherein the first treatment plan is generated based on attribute datacomprising an eating or drinking schedule of the user, informationpertaining to an age of the user, information pertaining to a sex of theuser, information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed,information pertaining to sexual performance health issues of otherusers, or some combination thereof.

Clause 7.12 The computer-implemented system of any clause herein,wherein the transmitting the data further comprises transmitting thedata to a second computing device that relays the data to a thirdcomputing device of a healthcare professional.

Clause 8.12 The computer-implemented system of any clause herein,wherein the data comprises a procedure performed on the user, anelectronic medical record associated with the user, a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a respiration rate of theuser, spirometry data related to the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a bariatric diagnosis of theuser, a pathological diagnosis related to a prostate gland or urogenitaltract of the user, or some combination thereof.

Clause 9.12 The computer-implemented system of any clause herein,wherein the sexual performance health issue of the user compriseserectile dysfunction, abnormally low or high testosterone levels,abnormally low or high estrogen levels, abnormally low or high progestinlevels, diminished libido, health conditions associated with abnormallevels of any of the foregoing hormones or of other hormones, or somecombination thereof.

Clause 10.12 A computer-implemented method comprising:

-   -   receive, at a computing device, a first treatment plan designed        to treat a sexual performance health issue of a user, wherein        the first treatment plan comprises at least two exercise        sessions that, based on the sexual performance health issue of        the user, enable the user to perform an exercise at different        exertion levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive data from one or more        sensors configured to measure the data associated with the        sexual performance health issue of the user, wherein the        electromechanical machine is configured to be manipulated by the        user while the user performs the first treatment plan;    -   transmit the data, wherein one or more machine learning models        are used to generate a second treatment plan, wherein the second        treatment plan modifies at least one exertion level, and the        modification is based on a standardized measure comprising        perceived exertion, the data, and the sexual performance health        issue of the user; and    -   receive the second treatment plan.

Clause 11.12 The computer-implemented method of any clause herein,wherein the data comprises information pertaining to cardiac health ofthe user, oncologic health of the user, pulmonary health of the user,bariatric health of the user, rehabilitation from pathologies related toa prostate gland or urogenital tract, or some combination thereof.

Clause 12.12 The computer-implemented method of any clause herein,wherein the second treatment plan comprises a modified parameterpertaining to the electromechanical machine, wherein the modifiedparameter comprises a resistance, a range of motion, a length of time,an angle of a component of the electromechanical machine, a speed, avelocity, an angular velocity, an acceleration, a torque, or somecombination thereof or some combination thereof, and thecomputer-implemented system further comprises:

-   -   controlling the electromechanical machine based on the modified        parameter.

Clause 13.12 The computer-implemented method of any clause herein,wherein the standardized measure of perceived exertion comprises ametabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE).

Clause 14.12 The computer-implemented method of any clause herein,wherein, by predicting exercises that will result in the desiredexertion level for each session, the one or more machine learning modelsgenerate the second treatment plan, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' data, and other users' sexualperformance health issues.

Clause 15.12 The computer-implemented method of any clause herein,wherein the first treatment plan is generated based on attribute datacomprising an eating or drinking schedule of the user, informationpertaining to an age of the user, information pertaining to a sex of theuser, information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed,information pertaining to sexual performance health issues of otherusers, or some combination thereof.

Clause 16.12 The computer-implemented method of any clause herein,wherein the transmitting the data further comprises transmitting thedata to a second computing device that relays the data to a thirdcomputing device of a healthcare professional.

Clause 17.12 The computer-implemented method of any clause herein,wherein the data comprises a procedure performed on the user, anelectronic medical record associated with the user, a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a respiration rate of theuser, spirometry data related to the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a bariatric diagnosis of theuser, a pathological diagnosis related to a prostate gland or urogenitaltract of the user, or some combination thereof.

Clause 18.12 The computer-implemented method of any clause herein,wherein the sexual performance health issue of the user compriseserectile dysfunction, abnormally low or high testosterone levels,abnormally low or high estrogen levels, abnormally low or high progestinlevels, diminished libido, health conditions associated with abnormallevels of any of the foregoing hormones or of other hormones, or somecombination thereof.

Clause 19.12 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive, at a computing device, a first treatment plan designed        to treat a sexual performance health issue of a user, wherein        the first treatment plan comprises at least two exercise        sessions that, based on the sexual performance health issue of        the user, enable the user to perform an exercise at different        exertion levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive data from one or more        sensors configured to measure the data associated with the        sexual performance health issue of the user, wherein the        electromechanical machine is configured to be manipulated by the        user while the user performs the first treatment plan;    -   transmit the data, wherein one or more machine learning models        are used to generate a second treatment plan, wherein the second        treatment plan modifies at least one exertion level, and the        modification is based on a standardized measure comprising        perceived exertion, the data, and the sexual performance health        issue of the user; and    -   receive the second treatment plan.

Clause 20.12 The computer-readable medium of any clause herein, whereinthe data comprises information pertaining to cardiac health of the user,oncologic health of the user, pulmonary health of the user, bariatrichealth of the user, rehabilitation from pathologies related to aprostate gland or urogenital tract, or some combination thereof.

System and Method for Using AI/ML and Telemedicine for Prostate-RelatedOncologic or Other Surgical Treatment to Determine a Cardiac TreatmentPlan that Uses Via an Electromechanical Machine, and Where ErectileDysfunction is Secondary to the Prostate Treatment and/or Condition

FIG. 28 generally illustrates an example embodiment of a method 2800 forusing artificial intelligence and machine learning and telemedicine forprostate-related oncologic or other surgical treatment to determine acardiac treatment plan that uses via an electromechanical machine, andwhere erectile dysfunction is secondary to the prostate treatment and/orcondition according to the principles of the present disclosure. Themethod 2800 may be performed by processing logic that may includehardware (circuitry, dedicated logic, etc.), software, or a combinationof both. The method 2800 and/or each of their individual functions,subroutines, or operations may be performed by one or more processingdevices of a computing device (e.g., the computer system 1100 of FIG. 11) implementing the method 2800. The method 2800 may be implemented ascomputer instructions stored on a memory device and executable by theone or more processing devices. In certain implementations, the method2800 may be performed by a single processing thread. Alternatively, themethod 2800 may be performed by two or more processing threads, eachthread implementing one or more individual functions, routines,subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 2800.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 2800.

At block 2802, the processing device may receive, at a computing device,a first treatment plan designed to treat a prostate-related health issueof a user. The first treatment plan may include at least two exercisesessions that, based on the prostate-related health issue of the user,enable the user to perform an exercise at different exertion levels. Insome embodiments, prostate-related information pertaining to the usermay be received from an application programming interface associatedwith an electronic medical records system. In some embodiments, theprostate-related health issue may include an oncologic health issue,another surgery-related health issue, or some combination thereof.

In some embodiments, the first treatment plan may be generated based onattribute data including an eating or drinking schedule of the user,information pertaining to an age of the user, information pertaining toa sex of the user, information pertaining to a weight of the userinformation pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed, orsome combination thereof.

At block 2704, while the user uses an electromechanical machine toperform the first treatment plan for the user, the processing device mayreceive data from one or more sensors configured to measure the dataassociated with the prostate-related health issue of the user. In someembodiments, the data may include a procedure performed on the user, anelectronic medical record associated with the user, a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a respiration rate of theuser, spirometry data related to the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a bariatric diagnosis of theuser, a pathological diagnosis related to a prostate gland or urogenitaltract of the user, or some combination thereof. Further, the data mayinclude information pertaining to cardiac health of the user, oncologichealth of the user, pulmonary health of the user, bariatric health ofthe user, rehabilitation from pathologies related to a prostate gland orurogenital tract, or some combination thereof.

At block 2706, the processing device may transmit the data. In someembodiments, one or more machine learning models 13 may be executed bythe server 30 and the machine learning models 13 may be used to generatea second treatment plan based on the data and/or the prostate-relatedhealth issues of users. The second treatment plan may modify at leastone exertion level, and the modification may be based on a standardizedmeasure including perceived exertion, the data, and the prostate-relatedhealth issue of the user. In some embodiments, the standardized measureof perceived exertion may include a metabolic equivalent of tasks (MET)or a Borg rating of perceived exertion (RPE).

In some embodiments, the one or more machine learning models generatethe second treatment plan by predicting exercises that will result inthe desired exertion level for each session. The one or more machinelearning models may be trained using data pertaining to the standardizedmeasure of perceived exertion, other users' data, and other users'prostate-related health issues as input data, and other users' exertionlevels that led to desired results as output data. The input data andthe output data may be labeled and mapped accordingly.

At block 2708, the processing device may receive the second treatmentplan from the server 30. The processing device may implement at least aportion of the treatment plan to cause an operating parameter of theelectromechanical machine to be modified in accordance with the modifiedexertion level set in the second treatment plan. To that end, in someembodiments, the second treatment plan may include a modified parameterpertaining to the electromechanical machine. The modified parameter mayinclude a resistance, a range of motion, a length of time, an angle of acomponent of the electromechanical machine, a speed, a velocity, anangular velocity, an acceleration, a torque, or some combinationthereof. The processing device may, based on the modified parameter,control the electromechanical machine.

In some embodiments, transmitting the data may include transmitting thedata to a second computing device that relays the prostate-relatedhealth issue data to a third computing device that is associated with ahealthcare professional.

Clauses

Clause 1.13 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user performs a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, at a computing device, a first treatment plan designed        to treat a prostate-related health issue of a user, wherein the        first treatment plan comprises at least two exercise sessions        that, based on the prostate-related health issue of the user,        enable the user to perform an exercise at different exertion        levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive data from one or more        sensors configured to measure the data associated with the        prostate-related health issue of the user;    -   transmit the data, wherein one or more machine learning models        are used to generate a second treatment plan, wherein the second        treatment plan modifies at least one exertion level, and the        modification is based on a standardized measure comprising        perceived exertion, the data, and the prostate-related health        issue of the user; and    -   receive the second treatment plan.

Clause 2.13 The computer-implemented system of any clause herein,wherein the data comprises information pertaining to cardiac health ofthe user, oncologic health of the user, pulmonary health of the user,bariatric health of the user, rehabilitation from pathologies related toa prostate gland or urogenital tract, or some combination thereof.

Clause 3.13 The computer-implemented system of any clause herein,wherein the prostate-related health issue further comprises an oncologichealth issue, another surgery-related health issue, or some combinationthereof.

Clause 4.13 The computer-implemented system of any clause herein,wherein the second treatment plan comprises a modified parameterpertaining to the electromechanical machine, wherein the modifiedparameter comprises a resistance, a range of motion, a length of time,an angle of a component of the electromechanical machine, a speed, avelocity, an angular velocity, an acceleration, a torque, or somecombination thereof, and the computer-implemented system furthercomprises:

-   -   controlling the electromechanical machine based on the modified        parameter.

Clause 5.13 The computer-implemented system of any clause herein,wherein the standardized measure of perceived exertion comprises ametabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE).

Clause 6.13 The computer-implemented system of any clause herein,wherein the one or more machine learning models generate the secondtreatment plan by predicting exercises that will result in the desiredexertion level for each session, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' data, and other users' prostate-relatedhealth issues.

Clause 7.13 The computer-implemented system of any clause herein,wherein the first treatment plan is generated based on attribute datacomprising an eating or drinking schedule of the user, informationpertaining to an age of the user, information pertaining to a sex of theuser, information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed,information pertaining to prostate-related health issues of other users,or some combination thereof.

Clause 8.13 The computer-implemented system of any clause herein,wherein the transmitting the data further comprises transmitting thedata to a second computing device that relays the data to a thirdcomputing device of a healthcare professional.

Clause 9.13 The computer-implemented system of any clause herein,wherein the data comprises a procedure performed on the user, anelectronic medical record associated with the user, a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a respiration rate of theuser, spirometry data related to the user, or some combination thereof.

Clause 10.13 A computer-implemented method comprising:

-   -   receive, at a computing device, a first treatment plan designed        to treat a prostate-related health issue of a user, wherein the        first treatment plan comprises at least two exercise sessions        that, based on the prostate-related health issue of the user,        enable the user to perform an exercise at different exertion        levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive data from one or more        sensors configured to measure the data associated with the        prostate-related health issue of the user, wherein the        electromechanical machine is configured to be used by the user        while performing the first treatment plan;    -   transmit the data, wherein one or more machine learning models        are used to generate a second treatment plan, wherein the second        treatment plan modifies at least one exertion level, and the        modification is based on a standardized measure comprising        perceived exertion, the data, and the prostate-related health        issue of the user; and    -   receive the second treatment plan.

Clause 11.13 The computer-implemented method of any clause herein,wherein the data comprises information pertaining to cardiac health ofthe user, oncologic health of the user, pulmonary health of the user,bariatric health of the user, rehabilitation from pathologies related toa prostate gland or urogenital tract, or some combination thereof.

Clause 12.13 The computer-implemented method of any clause herein,wherein the prostate-related health issue further comprises an oncologichealth issue, another surgery-related health issue, or some combinationthereof.

Clause 13.13 The computer-implemented method of any clause herein,wherein the second treatment plan comprises a modified parameterpertaining to the electromechanical machine, wherein the modifiedparameter comprises a resistance, a range of motion, a length of time,an angle of a component of the electromechanical machine, a speed, avelocity, an angular velocity, an acceleration, a torque, or somecombination thereof, and the computer-implemented system furthercomprises:

-   -   controlling the electromechanical machine based on the modified        parameter.

Clause 14.13 The computer-implemented method of any clause herein,wherein the standardized measure of perceived exertion comprises ametabolic equivalent of tasks (MET) or a Borg rating of perceivedexertion (RPE).

Clause 15.13 The computer-implemented method of any clause herein,wherein the one or more machine learning models generate the secondtreatment plan by predicting exercises that will result in the desiredexertion level for each session, and the one or more machine learningmodels are trained using data pertaining to the standardized measure ofperceived exertion, other users' data, and other users' prostate-relatedhealth issues.

Clause 16.13 The computer-implemented method of any clause herein,wherein the first treatment plan is generated based on attribute datacomprising an eating or drinking schedule of the user, informationpertaining to an age of the user, information pertaining to a sex of theuser, information pertaining to a gender of the user, an indication of amental state of the user, information pertaining to a genetic conditionof the user, information pertaining to a disease state of the user,information pertaining to a microbiome from one or more locations on orin the user, an indication of an energy level of the user, informationpertaining to a weight of the user, information pertaining to a heightof the user, information pertaining to a body mass index (BMI) of theuser, information pertaining to a family history of cardiovascularhealth issues of the user, information pertaining to comorbidities ofthe user, information pertaining to desired health outcomes of the userif the treatment plan is followed, information pertaining to predictedhealth outcomes of the user if the treatment plan is not followed,information pertaining to prostate-related health issues of other users,or some combination thereof.

Clause 17.13 The computer-implemented method of any clause herein,wherein the transmitting the data further comprises transmitting thedata to a second computing device that relays the data to a thirdcomputing device of a healthcare professional.

Clause 18.13 The computer-implemented method of any clause herein,wherein the data comprises a procedure performed on the user, anelectronic medical record associated with the user, a weight of theuser, a cardiac output of the user, a heartrate of the user, a heartrhythm of the user, a blood pressure of the user, a blood oxygen levelof the user, a cardiovascular diagnosis of the user, anon-cardiovascular diagnosis of the user, a pulmonary diagnosis of theuser, an oncologic diagnosis of the user, a respiration rate of theuser, spirometry data related to the user, or some combination thereof.

Clause 19.13 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive, at a computing device, a first treatment plan designed        to treat a prostate-related health issue of a user, wherein the        first treatment plan comprises at least two exercise sessions        that, based on the prostate-related health issue of the user,        enable the user to perform an exercise at different exertion        levels;    -   while the user uses an electromechanical machine to perform the        first treatment plan for the user, receive data from one or more        sensors configured to measure the data associated with the        prostate-related health issue of the user, wherein the        electromechanical machine is configured to be used by the user        while performing the first treatment plan;    -   transmit the data, wherein one or more machine learning models        are used to generate a second treatment plan, wherein the second        treatment plan modifies at least one exertion level, and the        modification is based on a standardized measure comprising        perceived exertion, the data, and the prostate-related health        issue of the user; and    -   receive the second treatment plan.

Clause 20.13 The computer-readable medium of any clause herein, whereinthe data comprises information pertaining to cardiac health of the user,oncologic health of the user, pulmonary health of the user, bariatrichealth of the user, rehabilitation from pathologies related to aprostate gland or urogenital tract, or some combination thereof.

System and Method for Determining, Based on Advanced Metrics of ActualPerformance on an Electromechanical Machine, Medical ProcedureEligibility in Order to Ascertain Survivability Rates and Measures ofQuality of Life Criteria

FIG. 29 generally illustrates an example embodiment of a method 2900 fordetermining, based on advanced metrics of actual performance on anelectromechanical machine, medical procedure eligibility in order toascertain survivability rates and measures of quality of life criteriaaccording to the principles of the present disclosure. The method 2900may be performed by processing logic that may include hardware(circuitry, dedicated logic, etc.), software, or a combination of both.The method 2900 and/or each of their individual functions, subroutines,or operations may be performed by one or more processing devices of acomputing device (e.g., the computer system 1100 of FIG. 11 )implementing the method 2900. The method 2900 may be implemented ascomputer instructions stored on a memory device and executable by theone or more processing devices. In certain implementations, the method2900 may be performed by a single processing thread. Alternatively, themethod 2900 may be performed by two or more processing threads, eachthread implementing one or more individual functions, routines,subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 2900.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 2900.

At block 2902, the processing device may receive, a set of datapertaining to users using one or more electromechanical machines toperform one or more treatment plans. The set of data may includeperformance data, personal data, measurement data, or some combinationthereof.

At block 2904, the processing device may determine, based on the data,one or more survivability rates of one or more procedures, one or morequality of life metrics, or some combination thereof.

At block 2906, the processing device may determine, using one or moremachine learning models, a probability that the user satisfies athreshold pertaining to the one or more survivability rates of the oneor more procedures, the one or more quality of life metrics, or somecombination thereof. In some embodiments, the processing device mayprescribe to the user the treatment plan associated with the one or moresurvivability rates, the one or more quality of life metrics, or somecombination thereof. In some embodiments, the processing device mayprescribe to the user the electromechanical machine associated with thetreatment plan.

At block 2908, the processing device may select, based on theprobability, the user for the one or more procedures.

In some embodiments, the processing device may initiate a telemedicinesession while the user performs the treatment plan. The telemedicinesession may include the processing device communicatively coupled to aprocessing device associated with a healthcare professional.

In some embodiments, the processing device may receive, via the patientinterface 50, input pertaining to a perceived level of difficulty of anexercise associated with the treatment plan. The processing device maymodify, based on the input, an operating parameter of theelectromechanical machine. The processing device may receive input, viathe patient interface 50, input pertaining to a level of the user'sanxiety, depression, pain, difficulty in performing the treatment plan,or some combination thereof. This input may be used to adjust thetreatment plan, determine an effectiveness of the treatment plan forusers having similar characteristics, or the like. The input may be usedto retrain the one or more machine learning models to determinesubsequent treatment plans, survivability rates, quality of lifemetrics, or some combination thereof.

Clauses

Clause 1.14 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user performs a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive a plurality of data pertaining to users using one or        more electromechanical machines to perform one or more treatment        plans, wherein the plurality of data comprises performance data,        personal data, measurement data, or some combination thereof;    -   determine, based on the data, one or more survivability rates of        one or more procedures, one or more quality of life metrics, or        some combination thereof;    -   determine, using one or more machine learning models, a        probability that the user satisfies a threshold pertaining to        the one or more survivability rates of the one or more        procedures, the one or more quality of life metrics, or some        combination thereof; and    -   select, based on the probability, the user for the procedure.

Clause 2.14 The computer-implemented system of any clause herein,wherein the processing device is further configured to prescribe to theuser the treatment plan associated with the one or more survivabilityrates, the one or more quality of life metrics, or some combinationthereof.

Clause 3.14 The computer-implemented system of any clause herein,wherein the processing device is further configured to prescribe to theuser the electromechanical machine associated with the treatment plan.

Clause 4.14 The computer-implemented system of any clause herein,wherein the processing device is further configured to initiate atelemedicine session while the user performs the treatment plan, whereinthe telemedicine session comprises the processing device communicativelycoupled to a processing device associated with a healthcareprofessional.

Clause 5.14 The computer-implemented system of any clause herein,wherein the interface is configured to receive input pertaining to aperceived level of difficulty of an exercise associated with thetreatment plan.

Clause 6.14 The computer-implemented system of any clause herein,wherein the processing device is further configured to modify, based onthe input, an operating parameter of the electromechanical machine.

Clause 7.14 The computer-implemented system of any clause herein,wherein the processing device is further configured to:

-   -   receive, via the interface, input pertaining to a level of the        user's anxiety, depression, pain, difficulty in performing the        treatment plan, or some combination thereof.

Clause 8.14 A computer-implemented method, comprising:

-   -   receiving a plurality of data pertaining to users using one or        more electromechanical machines to perform one or more treatment        plans, wherein the plurality of data comprises performance data,        personal data, measurement data, or some combination thereof;    -   determining, based on the data, one or more survivability rates        of one or more procedures, one or more quality of life metrics,        or some combination thereof;    -   determining, using one or more machine learning models, a        probability that the user satisfies a threshold pertaining to        the one or more survivability rates of the one or more        procedures, the one or more quality of life metrics, or some        combination thereof; and    -   selecting, based on the probability, the user for the procedure.

Clause 9.14 The computer-implemented method of any clause herein,further comprising prescribing to the user the treatment plan associatedwith the one or more survivability rates, the one or more quality oflife metrics, or some combination thereof.

Clause 10.14 The computer-implemented method of any clause herein,further comprising prescribing to the user the electromechanical machineassociated with the treatment plan.

Clause 11.14 The computer-implemented method of any clause herein,further comprising initiating a telemedicine session while the userperforms the treatment plan, wherein the telemedicine session comprisesthe processing device communicatively coupled to a processing deviceassociated with a healthcare professional.

Clause 12.14 The computer-implemented method of any clause herein,wherein the interface is configured to receive input pertaining to aperceived level of difficulty of an exercise associated with thetreatment plan.

Clause 13.14 The computer-implemented method of any clause herein,further comprising modifying, based on the input, an operating parameterof the electromechanical machine.

Clause 14.14 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, via the interface, input pertaining to a level of the        user's anxiety, depression, pain, difficulty in performing the        treatment plan, or some combination thereof.

Clause 15.14 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive a plurality of data pertaining to users using one or        more electromechanical machines to perform one or more treatment        plans, wherein the plurality of data comprises performance data,        personal data, measurement data, or some combination thereof;    -   determine, based on the data, one or more survivability rates of        one or more procedures, one or more quality of life metrics, or        some combination thereof;    -   determine, using one or more machine learning models, a        probability that the user satisfies a threshold pertaining to        the one or more survivability rates of the one or more        procedures, the one or more quality of life metrics, or some        combination thereof; and    -   select, based on the probability, the user for the procedure.

Clause 16.14 The computer-readable medium of any clause herein, whereinthe processing device is further configured to prescribe to the user thetreatment plan associated with the one or more survivability rates, theone or more quality of life metrics, or some combination thereof.

Clause 17.14 The computer-readable medium of any clause herein, whereinthe processing device is further configured to prescribe to the user theelectromechanical machine associated with the treatment plan.

Clause 18.14 The computer-readable medium of any clause herein, whereinthe processing device is further configured to initiate a telemedicinesession while the user performs the treatment plan, wherein thetelemedicine session comprises the processing device communicativelycoupled to a processing device associated with a healthcareprofessional.

Clause 19.14 The computer-readable medium of any clause herein, whereinthe interface is configured to receive input pertaining to a perceivedlevel of difficulty of an exercise associated with the treatment plan.

Clause 20.14 The computer-readable medium of any clause herein, furthercomprising modifying, based on the input, an operating parameter of theelectromechanical machine.

System and Method for Using AI/ML and Telemedicine to IntegrateRehabilitation for a Plurality of Comorbid Conditions

FIG. 30 generally illustrates an example embodiment of a method 3000 forusing artificial intelligence and machine learning and telemedicine tointegrate rehabilitation for a plurality of comorbid conditionsaccording to the principles of the present disclosure. The method 3000may be performed by processing logic that may include hardware(circuitry, dedicated logic, etc.), software, or a combination of both.The method 3000 and/or each of their individual functions, subroutines,or operations may be performed by one or more processing devices of acomputing device (e.g., the computer system 1100 of FIG. 11 )implementing the method 3000. The method 3000 may be implemented ascomputer instructions stored on a memory device and executable by theone or more processing devices. In certain implementations, the method3000 may be performed by a single processing thread. Alternatively, themethod 3000 may be performed by two or more processing threads, eachthread implementing one or more individual functions, routines,subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 3000.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 3000.

At block 3002, the processing device may receive, at a computing device,one or more characteristics of the user. The one or more characteristicsof the user may pertain to performance data, personal data, measurementdata, or some combination thereof. In some embodiments, a computingdevice associated with a healthcare professional may monitor the one ormore characteristics of the user while the user performs the treatmentplan in real-time or near real-time.

At block 3004, the processing device may determine, based on the one ormore characteristics of the user, a set of comorbid conditionsassociated with the user. In some embodiments, the set of comorbidconditions may be related to cardiac, orthopedic, pulmonary, bariatric,oncologic, or some combination thereof.

At block 3006, the processing device may determine, using one or moremachine learning models, the treatment plan for the user. Based on theone or more characteristics of the user and one or more similarcharacteristics of one or more other users, the one or more machinelearning models determine the treatment plan.

At block 3008, the processing device may control, based on the treatmentplan, the electromechanical machine.

In some embodiments, based on the one or more characteristics satisfyinga threshold, the processing device may initiate a telemedicine sessionbased on the one or more characteristics satisfying a threshold.

In some embodiments, the processing device may use the one or moremachine learning models to determine one or more exercises to include inthe treatment plan. The one or more exercises are determined based on anumber of conditions they treat, based on whether the one or moreexercises treat a most severe condition associated with the user, orbased on some combination thereof.

In some embodiments, the processing device may receive, from the patientinterface 50, input pertaining to a perceived level of difficulty of theuser performing the treatment plan. The processing device may modify,based on the input, an operating parameter of the electromechanicalmachine.

Clauses

Clause 1.15 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user performs a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, at a computing device, one or more characteristics of        the user, wherein the one or more characteristics pertain to        performance data, personal data, measurement data, or some        combination thereof;    -   determine, based on the one or more characteristics of the user,        a plurality of comorbid conditions associated with the user;    -   determine, using one or more machine learning models, the        treatment plan for the user, wherein, based on the one or more        characteristics of the user and one or more similar        characteristics of one or more other users, the one or more        machine learning models determine the treatment plan; and    -   control, based on the treatment plan, the electromechanical        machine.

Clause 2.15 The computer-implemented system of any preceding clause,wherein the plurality of comorbid conditions is related to cardiac,orthopedic, pulmonary, bariatric, oncologic, or some combinationthereof.

Clause 3.15 The computer-implemented system of any preceding clause,wherein a computing device associated with a healthcare professional maymonitor the one or more characteristics of the user while the userperforms the treatment plan in real-time or near real-time.

Clause 4.15 The computer-implemented system of any preceding clause,wherein, based on the one or more characteristics satisfying athreshold, the processing device is further configured to initiate atelemedicine session based on the one or more characteristics satisfyinga threshold.

Clause 5.15 The computer-implemented system of any preceding clause,wherein the processing device is further configured to use the one ormore machine learning models to determine one or more exercises toinclude in the treatment plan, wherein the one or more exercises aredetermined based on a number of conditions they treat, based on whetherthe one or more exercises treat a most severe condition associated withthe user, or based on some combination thereof.

Clause 6.15 The computer-implemented system of any preceding clause,wherein the processing device is further configured to receive inputpertaining to a perceived level of difficulty of the user performing thetreatment plan.

Clause 7.15 The computer-implemented system of any preceding clause,wherein the processing device is further configured to modify, based onthe input, an operating parameter of the electromechanical machine.

Clause 8.15 A computer-implemented method comprising:

-   -   receiving, at a computing device, one or more characteristics of        a user, wherein the one or more characteristics pertain to        performance data, personal data, measurement data, or some        combination thereof;    -   determining, based on the one or more characteristics of the        user, a plurality of comorbid conditions associated with the        user;    -   determining, using one or more machine learning models, the        treatment plan for the user, wherein, based on the one or more        characteristics of the user and one or more similar        characteristics of one or more other users, the one or more        machine learning models determine the treatment plan; and    -   controlling, based on the treatment plan, an electromechanical        machine, wherein the electromechanical machine is configured to        be manipulated by the user while the user performs the treatment        plan.

Clause 9.15 The computer-implemented method of any preceding clause,wherein the plurality of comorbid conditions is related to cardiac,orthopedic, pulmonary, bariatric, oncologic, or some combinationthereof.

Clause 10.15 The computer-implemented method of any preceding clause,wherein a computing device associated with a healthcare professional maymonitor the one or more characteristics of the user while the userperforms the treatment plan in real-time or near real-time.

Clause 11.15 The computer-implemented method of any preceding clause,wherein, based on the one or more characteristics satisfying athreshold, the processing device is further configured to initiate atelemedicine session based on the one or more characteristics satisfyinga threshold.

Clause 12.15 The computer-implemented method of any preceding clause,further comprising using the one or more machine learning models todetermine one or more exercises to include in the treatment plan,wherein the one or more exercises are determined based on a number ofconditions they treat, based on whether the one or more exercises treata most severe condition associated with the user, or based on somecombination thereof.

Clause 13.15 The computer-implemented method of any preceding clause,further comprising receiving input pertaining to a perceived level ofdifficulty of the user performing the treatment plan.

Clause 14.15 The computer-implemented method of any preceding clause,further comprising modifying, based on the input, an operating parameterof the electromechanical machine.

Clause 15.15 A tangible, non-transitory computer-readable storinginstructions that, when executed, cause a processing device to:

-   -   receive, at a computing device, one or more characteristics of        the user, wherein the one or more characteristics pertain to        performance data, personal data, measurement data, or some        combination thereof;    -   determine, based on the one or more characteristics of the user,        a plurality of comorbid conditions associated with the user;    -   determine, using one or more machine learning models, the        treatment plan for the user, wherein, based on the one or more        characteristics of the user and one or more similar        characteristics of one or more other users, the one or more        machine learning models determine the treatment plan; and    -   control, based on the treatment plan, an electromechanical        machine, wherein the electromechanical machine is configured to        be manipulated by a user while the user performs the treatment        plan.

Clause 16.15 The computer-readable medium of any preceding clause,wherein the plurality of comorbid conditions is related to cardiac,orthopedic, pulmonary, bariatric, oncologic, or some combinationthereof.

Clause 17.15 The computer-readable medium of any preceding clause,wherein a computing device associated with a healthcare professional maymonitor the one or more characteristics of the user while the userperforms the treatment plan in real-time or near real-time.

Clause 18.15 The computer-readable medium of any preceding clause,wherein, based on the one or more characteristics satisfying athreshold, the processing device is further configured to initiate atelemedicine session based on the one or more characteristics satisfyinga threshold.

Clause 19.15 The computer-readable medium of any preceding clause,wherein the processing device is to use the one or more machine learningmodels to determine one or more exercises to include in the treatmentplan, wherein the one or more exercises are determined based on a numberof conditions they treat, based on whether the one or more exercisestreat a most severe condition associated with the user, or based on somecombination thereof.

Clause 20.15 The computer-readable medium of any preceding clause,wherein the processing device is to receive input pertaining to aperceived level of difficulty of the user performing the treatment plan.

System and Method for Using AI/ML and Generic Risk Factors to ImproveCardiovascular Health Such that the Need for Additional CardiacInterventions in Response to Cardiac-Related Events (CREs) is Mitigated

FIG. 31 generally illustrates an example embodiment of a method 3100 forusing artificial intelligence and machine learning and generic riskfactors to improve cardiovascular health such that the need for cardiacintervention is mitigated according to the principles of the presentdisclosure. The method 3100 is configured to generate, provide, and/oradjust a treatment plan for a user who has experienced a cardiac-relatedevent or who is likely, in a probabilistic sense or according to aprobabilistic metric, whether parametric or non-parametric, toexperience a cardiac-related event, including but not limited to CREsarising out of existing or incipient cardiac conditions. The method 3100may be performed by processing logic that may include hardware(circuitry, dedicated logic, etc.), software, or a combination of both.The method 3100 and/or each of their individual functions, subroutines,or operations may be performed by one or more processing devices of acomputing device (e.g., the system 10 of FIG. 1 , the computer system1100 of FIG. 11 , etc.) implementing the method 3100. The method 3100may be implemented as computer instructions stored on a memory deviceand executable by the one or more processing devices. In certainimplementations, the method 3100 may be performed by a single processingthread. Alternatively, the method 3100 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the methods.

In some embodiments, a system may be used to implement the method 3100.The system may include the treatment apparatus 70 (e.g., anelectromechanical machine) configured to be manipulated by a user whilethe user is performing a treatment plan, and an interface including adisplay configured to present information pertaining to the treatmentplan. The system may include a processing device configured to executeinstructions implemented the method 3100.

At block 3102, the processing device may receive, from one or more datasources, information pertaining to the user. The information may includeone or more risk factors associated with a cardiac-related event for theuser. In some embodiments, the one or more risk factors may includegenetic history of the user, medical history of the user, familialmedical history of the user, demographics of the user, a cohort orcohorts of the user, psychographics of the user, behavior history of theuser, or some combination thereof. The one or more data sources mayinclude an electronic medical record system, an application programminginterface, a third-party application, a sensor, a website, or somecombination thereof.

At block 3104, the processing device may generate, using one or moretrained machine learning models, the treatment plan for the user. Thetreatment plan may be generated based on the information pertaining tothe user, and the treatment plan may include one or more exercisesassociated with managing the one or more risk factors to reduce aprobability of a cardiac intervention for the user.

At block 3106, the processing device may transmit the treatment plan tocause the electromechanical machine to implement the one or moreexercises. In some embodiments, the processing device may modify anoperating parameter of the electromechanical machine to case theelectromechanical machine to implement the one or more exercises. Insome embodiments, the processing device may initiate, while the userperforms the treatment plan, a telemedicine session between a computingdevice of the user and a computing device of a healthcare professional.

In some embodiments, the processing device may receive, from one or moresensors, one or more measurements associated with the user. The one ormore measurements may be received while the user performs the treatmentplan. The processing device may determine, based on the one or moremeasurements, whether the one or more risk factors are being managedwithin a desired range. For example, the processing device determineswhether characteristics (e.g., a heartrate) of the user while performingthe treatment plan meet thresholds for addressing (e.g., improving,reducing, etc.) the risk factors. In some embodiments, a trained machinelearning model 13 may be used to receive the measurements as input andto output a probability that one or more of the risk factors are beingmanaged within a desired range or are not being managed within thedesired range.

In some embodiments, responsive to determining the one or more riskfactors are being managed within the desired range, the processingdevice is to control the electromechanical machine according to thetreatment plan. In some embodiments, responsive to determining the oneor more risk factors are not being managed within the desired range, theprocessing device may modify, using the one or more trained machinelearning models, the treatment plan to generate a modified treatmentplan including at least one modified exercise. In some embodiments, theprocessing device may transmit the modified treatment plan to cause theelectromechanical machine to implement the at least one modifiedexercise.

Clauses

Clause 1.16 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while the user performs a treatment plan;    -   an interface comprising a display configured to present        information associated with the treatment plan; and    -   a processing device configured to:    -   receive, from one or more data sources, information associated        with the user, wherein the information comprises one or more        risk factors associated with a cardiac-related event for the        user;    -   generate, using one or more trained machine learning models, the        treatment plan for the user, wherein the treatment plan is        generated based on the information associated with the user, and        the treatment plan comprises one or more exercises associated        with managing the one or more risk factors to reduce a        probability of the cardiac intervention for the user; and    -   transmit the treatment plan to cause the electromechanical        machine to implement the one or more exercises.

Clause 2.16 The computer-implemented system of any clause herein,wherein the one or more risk factors comprise genetic history of theuser, medical history of the user, familial medical history of the user,demographics of the user, psychographics of the user, behavior historyof the user, or some combination thereof.

Clause 3.16 The computer-implemented system of any clause herein,wherein the processing device is further to:

-   -   receive, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan; and    -   determine, based on the one or more measurements, whether the        one or more risk factors are being managed within a desired        range.

Clause 4.16 The computer-implemented system of any clause herein,wherein, responsive to determining the one or more risk factors arebeing managed within the desired range, the processing device is tocontrol the electromechanical device according to the treatment plan.

Clause 5.16 The computer-implemented system of any clause herein,wherein, responsive to determining the one or more risk factors are notbeing managed within the desired range, the processing device is to:

-   -   modify, using the one or more trained machine learning models,        the treatment plan to generate a modified treatment plan        comprising at least one modified exercise, and    -   transmit the modified treatment plan to cause the        electromechanical machine to implement the at least one modified        exercise.

Clause 6.16 The computer-implemented system of any clause herein,wherein the one or more data sources comprise an electronic medicalrecord system, an application programming interface, a third-partyapplication, a sensor, a website, or some combination thereof.

Clause 7.16 The computer-implemented system of any clause herein,wherein the processing device is to modify an operating parameter of theelectromechanical machine to cause the electromechanical machine toimplement the one or more exercises.

Clause 8.16 The computer-implemented system of any clause herein,wherein the processing device is to initiate, while the user performsthe treatment plan, a telemedicine session between a computing device ofthe user and a computing device of a healthcare professional.

Clause 9.16 A computer-implemented method, comprising:

-   -   receiving, from one or more data sources, information associated        with a user, wherein the information comprises one or more risk        factors associated with a cardiac-related event for the user;    -   generating, using one or more trained machine learning models, a        treatment plan for the user, wherein the treatment plan is        generated based on the information associated with the user, and        the treatment plan comprises one or more exercises associated        with managing the one or more risk factors to reduce a        probability of the cardiac intervention for the user; and    -   transmitting the treatment plan to cause an electromechanical        machine to implement the one or more exercises, the        electromechanical machine configured to be manipulated by the        user while the user performs the treatment plan.

Clause 10.16 The computer-implemented method of any clause herein,wherein the one or more risk factors comprise genetic history of theuser, medical history of the user, familial medical history of the user,demographics of the user, psychographics of the user, behavior historyof the user, or some combination thereof.

Clause 11.16 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan; and    -   determining, based on the one or more measurements, whether the        one or more risk factors are being managed within a desired        range.

Clause 12.16 The computer-implemented method of any clause herein,wherein, responsive to determining the one or more risk factors arebeing managed within the desired range, the method further comprisescontrolling the electromechanical device according to the treatmentplan.

Clause 13.16 The computer-implemented method of any clause herein,wherein, responsive to determining the one or more risk factors are notbeing managed within the desired range, the method further comprises:

-   -   modifying, using the one or more trained machine learning        models, the treatment plan to generate a modified treatment plan        comprising at least one modified exercise, and    -   transmitting the modified treatment plan to cause the        electromechanical machine to implement the at least one modified        exercise.

Clause 14.16 The computer-implemented method of any clause herein,wherein the one or more data sources comprise an electronic medicalrecord system, an application programming interface, a third-partyapplication, a sensor, a website, or some combination thereof.

Clause 15.16 The computer-implemented method of any clause herein,further comprising modifying an operating parameter of theelectromechanical machine to cause the electromechanical machine toimplement the one or more exercises.

Clause 16.16 The computer-implemented method of any clause herein,further comprising initiating, while the user performs the treatmentplan, a telemedicine session between a computing device of the user anda computing device of a healthcare professional.

Clause 17.16 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive, from one or more data sources, information associated        with a user, wherein the information comprises one or more risk        factors associated with a cardiac-related event;    -   generate, using one or more trained machine learning models, a        treatment plan for the user, wherein the treatment plan is        generated based on the information associated with the user, and        the treatment plan comprises one or more exercises associated        with managing the one or more risk factors to reduce a        probability of the cardiac intervention for the user; and    -   transmit the treatment plan to cause an electromechanical        machine to implement the one or more exercises, the        electromechanical machine configured to be manipulated by the        user while the user performs the treatment plan;    -   Clause 18.16 The computer-readable medium of any clause herein,        wherein the one or more risk factors comprise genetic history of        the user, medical history of the user, familial medical history        of the user, demographics of the user, psychographics of the        user, behavior history of the user, or some combination thereof.

Clause 19.16 The computer-readable medium of any clause herein, whereinthe processing device is further to:

-   -   receive, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan; and    -   determine, based on the one or more measurements, whether the        one or more risk factors are being managed within a desired        range.

Clause 20.16 The computer-readable medium of any clause herein, wherein,responsive to determining the one or more risk factors are being managedwithin the desired range, the processing device is further to controlthe electromechanical device according to the treatment plan.

Clause 21. 16 A computer-implemented system, comprising:

-   -   a processing device configured to receive a plurality of risk        factors associated with a cardiac-related event for a user,    -   generate a selected set of the risk factors,    -   determine, based on the selected set of the risk factors, a        probability that a cardiac intervention will occur, and    -   generate, based on the probability and the selected set of the        risk factors, a treatment plan including one or more exercises        directed to reducing the probability that the cardiac        intervention will occur; and    -   a treatment apparatus configured to implement the treatment plan        while the treatment apparatus is being manipulated by the user.

Clause 22.16 The computer-implemented system of any clause herein,wherein the processing device is configured to execute a risk factormodel, and wherein, to generate the selected set of the risk factors,the risk factor model is configured to at least one of assign weights tothe risk factors, rank the risk factors, and filter the risk factors.

Clause 23.16 The computer-implemented system of any clause herein,wherein the processing device is configured to execute a probabilitymodel, wherein the probability model is configured to determine theprobability that the cardiac intervention will occur.

Clause 24.16 The computer-implemented system of any clause herein,wherein the probability model is configured to determine the probabilitybased on respective probabilities associated with individual ones of theselected set of the risk factors.

Clause 25.16 The computer-implemented system of any clause herein,wherein the processing device is configured to execute a treatment planmodel, wherein the treatment plan model is configured to generate thetreatment plan based on individual probabilities of the cardiacintervention of respective ones of the selected set of the risk factors.

Clause 26.16 The computer-implemented system of any clause herein,wherein the treatment plan model is configured to generate the treatmentplan based on an identified one of the selected set of the risk factorshaving a largest contribution to the probability that the cardiacintervention will occur.

Clause 27.16 The computer-implemented system of any clause herein,wherein, subsequent to implementing the treatment plan using thetreatment apparatus, the processing device is configured to modify thetreatment plan based on a determination of whether the treatment planreduced either one of the probability that the cardiac intervention willoccur and the identified one of the selected set of risk factors.

Clause 28.16 The computer-implemented system of any clause herein,wherein the processing device is configured to transmit the modifiedtreatment plan to cause the treatment apparatus to implement at leastone modified exercise of the modified treatment plan.

Clause 29.16 The computer-implemented system of any clause herein,wherein the cardiac intervention is for minimizing one or more negativeeffects of the cardiac-related event.

Clause 30.16 The computer-implemented system of any clause herein,wherein the processing device is configured to initiate, while the userperforms the treatment plan, a telemedicine session between a computingdevice of the user and a computing device of a healthcare professional.

Clause 31.16 The computer-implemented system of any clause herein,wherein the one or more risk factors comprise modifiable risk factorsand non-modifiable risk factors.

Clause 32.16 A computer-implemented method, comprising:

-   -   receiving a plurality of risk factors associated with a        cardiac-related event for a user;    -   generating a selected set of the risk factors;    -   determining a probability that a cardiac intervention will occur        based on the selected set of the risk factors;    -   generating, based on the probability and the selected set of the        risk factors, a treatment plan including one or more exercises        directed to reducing the probability that the cardiac        intervention will occur; and    -   using a treatment apparatus to implement the treatment plan        while the treatment apparatus being manipulated by the user.

Clause 33.16 The computer-implemented method of any clause herein,further comprising:

-   -   using a risk factor machine learning model to generate the        selected set of the risk factors, wherein the risk factor model        is configured to at least one of assign weights to the risk        factors, rank the risk factors, and filter the risk factors; and    -   using a probability machine learning model to determine the        probability that the cardiac intervention will occur.

Clause 34.16 The computer-implemented method of any clause herein,further comprising using the probability machine learning model todetermine the probability based on respective probabilities associatedwith individual ones of the selected set of the risk factors.

Clause 35.16 The computer-implemented method of any clause herein,further comprising using a treatment plan machine learning model togenerate the treatment plan based on individual probabilities of thecardiac intervention of respective ones of the selected set of the riskfactors.

Clause 36.16 The computer-implemented method of any clause herein,further comprising generating the treatment plan based on an identifiedone of the selected set of the risk factors having a largestcontribution to the probability that the cardiac intervention willoccur.

Clause 37.16 The computer-implemented method of any clause herein,further comprising, subsequent to implementing the treatment plan usingthe treatment apparatus, modifying the treatment plan based on adetermination of whether the treatment plan reduced either one of (i)the probability that the cardiac intervention will occur and (ii) theidentified one of the selected set of the risk factors.

Clause 38.16 The computer-implemented method of any clause herein,wherein the cardiac intervention is for minimizing one or more negativeeffects of the cardiac-related event.

Clause 39.16 The computer-implemented method of any clause herein,wherein the one or more risk factors comprise modifiable risk factorsand non-modifiable risk factors.

System and Method for Using AI/ML to Generate Treatment Plans toStimulate Preferred Angiogenesis

Systems and methods implementing the principles of the presentdisclosure as described below in more detail are configured to generatetreatment plans to trigger or stimulate (or, in some examples, inhibit)angiogenesis. As used herein, “angiogenesis” refers to the formation andgrowth of new blood vessels. “Inhibit,” as used herein, may refer toactively preventing angiogenesis or simply avoiding treatment plans,exercises, exercise conditions, etc. that trigger or stimulateangiogenesis.

FIG. 32 generally illustrates an example embodiment of a method 3200 forusing artificial intelligence and machine learning to generate treatmentplans to stimulate preferred angiogenesis according to the principles ofthe present disclosure. The method 3200 may be performed by processinglogic that may include hardware (circuitry, dedicated logic, etc.),software, or a combination of both. The method 3200 and/or each of theirindividual functions, subroutines, or operations may be performed by oneor more processing devices of a computing device (e.g., the system ofFIG. 1 , the computer system 1100 of FIG. 11 , etc.) implementing themethod 3200. The method 3200 may be implemented as computer instructionsstored on a memory device and executable by the one or more processingdevices. In certain implementations, the method 3200 may be performed bya single processing thread. Alternatively, the method 3200 may beperformed by two or more processing threads, each thread implementingone or more individual functions, routines, subroutines, or operationsof the methods.

In some embodiments, a system may be used to implement the method 3200.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 3200.

At block 3202, the processing device may receive, from one or more datasources, information associated with the user. The information may beassociated with one or more characteristics of the user's blood vessels.The information may be associated with blockage of at least one of theblood vessels of the user, familial history of blood vessel disease,heartrate of the user, blood pressure of the user, or some combinationthereof. The one or more data sources may include an electronic medicalrecord system, an application programming interface, a third-partyapplication, or some combination thereof. The received information mayalso include other information associated with the user (“usercharacteristics”), including, but not limited to, personal, family,and/or other health-related information as defined above in more detail.In some examples, the received information may include informationindicative of one or more conditions for which angiogenesis is preferredor desirable (e.g., certain cardiac conditions) or not desirable (e.g.,certain oncological conditions).

At block 3204, the processing device may generate, using one or moretrained machine learning models, the treatment plan for the user. Thetreatment plan may be generated based on the information associated withthe user and the treatment plan includes one or more exercisesassociated with triggering (or inhibiting) angiogenesis in at least oneof the user's blood vessels.

At block 3206, the processing device may transmit the treatment plan tocause the electromechanical machine to implement the one or moreexercises. In some embodiments, the processing device may modify anoperating parameter of the electromechanical machine to cause theelectromechanical machine to implement the one or more exercises. Insome embodiments, the processing device may initiate, while the userperforms the treatment plan, a telemedicine session between a computingdevice of the user and a computing device of a healthcare professional.

In some embodiments, the processing device may receive, from one or moresensors, one or more measurements associated with the user. The one ormore measurements may be received while the user performs the treatmentplan. The processing device may determine, based on the one or moremeasurements, whether predetermined criteria for the user's bloodvessels are satisfied. In some embodiments, a trained machine learningmodel 13 may be used to receive the measurements as input and to outputa probability that the treatment plan will stimulate (or inhibit)angiogenesis in at least one of the user's blood vessels.

In some embodiments, responsive to determining the predeterminedcriteria for the user's blood vessels are satisfied, the processingdevice may control the electromechanical machine according to thetreatment plan. Responsive to determining the predetermined criteria forthe user's blood vessels is not satisfied, the processing device maymodify, using the one or more trained machine learning models, thetreatment plan to generate a modified treatment plan including at leastone modified exercise. The processing device may transmit the modifiedtreatment plan to cause the electromechanical machine to implement theat least one modified exercise.

Clauses

Clause 1.17 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while performing a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive, from one or more data sources, information pertaining        to the user, wherein the information is associated with one or        more characteristics of the user's blood vessels;    -   generate, using one or more trained machine learning models, a        treatment plan for the user, wherein the treatment plan is        generated based on the information pertaining to the user, and        the treatment plan comprises one or more exercises associated        with triggering angiogenesis in at least one of the user's blood        vessels; and    -   transmit the treatment plan to cause the electromechanical        machine to implement the one or more exercises.

Clause 2.17 The computer-implemented system of any clause herein,wherein the information pertains to blockage of at least one of theblood vessels of the user, familial history blood vessel disease of theuser, heartrate of the user, blood pressure of the user, or somecombination thereof.

Clause 3.17 The computer-implemented system of any clause herein,wherein the processing device is further to:

-   -   receive, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan; and    -   determine, based on the one or more measurements, whether a        predetermined criteria for the user's blood vessels is        satisfied.

Clause 4.17 The computer-implemented system of any clause herein,wherein, responsive to determining the predetermined criteria for theuser's blood vessels is satisfied, the processing device is to controlthe electromechanical device according to the treatment plan.

Clause 5.17 The computer-implemented system of any clause herein,wherein, responsive to determining the predetermined criteria for theuser's blood vessels is not satisfies, the processing device is to:

-   -   modify, using the one or more trained machine learning models,        the treatment plan to generate a modified treatment plan        comprising at least one modified exercise, and    -   transmit the modified treatment plan to cause the        electromechanical machine to implement the at least one modified        exercise.

Clause 6.17 The computer-implemented system of any clause herein,wherein the one or more data sources comprise an electronic medicalrecord system, an application programming interface, a third-partyapplication, or some combination thereof.

Clause 7.17 The computer-implemented system of any clause herein,wherein the processing device is to modify an operating parameter of theelectromechanical machine to cause the electromechanical machine toimplement the one or more exercises.

Clause 8.17 The computer-implemented system of any clause herein,wherein the processing device is to initiate, while the user performsthe treatment plan, a telemedicine session between a computing device ofthe user and a computing device of a healthcare professional.

Clause 9.17 A computer-implemented method, comprising:

-   -   receiving, from one or more data sources, information pertaining        to the user, wherein the information is associated with one or        more characteristics of the user's blood vessels;    -   generating, using one or more trained machine learning models, a        treatment plan for a user, wherein the treatment plan is        generated based on the information pertaining to the user, and        the treatment plan comprises one or more exercises associated        with triggering angiogenesis in at least one of the user's blood        vessels; and    -   transmitting the treatment plan to cause an electromechanical        machine to implement the one or more exercises.

Clause 10.17 The computer-implemented method of any clause herein,wherein the information pertains to blockage of at least one of theblood vessels of the user, familial history blood vessel disease of theuser, heartrate of the user, blood pressure of the user, or somecombination thereof.

Clause 11.17 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan; and    -   determining, based on the one or more measurements, whether a        predetermined criteria for the user's blood vessels is        satisfied.

Clause 12.17 The computer-implemented method of any clause herein,wherein, responsive to determining the predetermined criteria for theuser's blood vessels is satisfied, the method further comprisescontrolling the electromechanical device according to the treatmentplan.

Clause 13.17 The computer-implemented method of any clause herein,wherein, responsive to determining the predetermined criteria for theuser's blood vessels is not satisfies, the method further comprises:

-   -   modifying, using the one or more trained machine learning        models, the treatment plan to generate a modified treatment plan        comprising at least one modified exercise, and    -   transmitting the modified treatment plan to cause the        electromechanical machine to implement the at least one modified        exercise.

Clause 14.17 The computer-implemented method of any clause herein,wherein the one or more data sources comprise an electronic medicalrecord system, an application programming interface, a third-partyapplication, or some combination thereof.

Clause 15.17 The computer-implemented method of any clause herein,wherein the processing device is to modify an operating parameter of theelectromechanical machine to cause the electromechanical machine toimplement the one or more exercises.

Clause 16.17 The computer-implemented method of any clause herein,further comprising initiating, while the user performs the treatmentplan, a telemedicine session between a computing device of the user anda computing device of a healthcare professional.

Clause 17.17 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive, from one or more data sources, information pertaining        to the user, wherein the information is associated with one or        more characteristics of the user's blood vessels;    -   generate, using one or more trained machine learning models, a        treatment plan for a user, wherein the treatment plan is        generated based on the information pertaining to the user, and        the treatment plan comprises one or more exercises associated        with triggering angiogenesis in at least one of the user's blood        vessels; and    -   transmit the treatment plan to cause an electromechanical        machine to implement the one or more exercises.

Clause 18.17 The computer-readable medium of any clause herein, whereinthe information pertains to blockage of at least one of the bloodvessels of the user, familial history blood vessel disease of the user,heartrate of the user, blood pressure of the user, or some combinationthereof.

Clause 19.17 The computer-readable medium of any clause herein, whereinthe processing device is to:

-   -   receive, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan; and    -   determine, based on the one or more measurements, whether a        predetermined criteria for the user's blood vessels is        satisfied.

Clause 20.17 The computer-readable medium of any clause herein, wherein,responsive to determining the predetermined criteria for the user'sblood vessels is satisfied, the processing device controls theelectromechanical device according to the treatment plan.

System and Method for Using AI/ML to Generate Treatment Plans IncludingTailored Dietary Plans for Users

FIG. 33 generally illustrates an example embodiment of a method 3300 forusing artificial intelligence and machine learning to generate treatmentplans including tailored dietary plans for users according to theprinciples of the present disclosure. The method 3300 may be performedby processing logic that may include hardware (circuitry, dedicatedlogic, etc.), software, or a combination of both. The method 3300 and/oreach of their individual functions, subroutines, or operations may beperformed by one or more processing devices of a computing device (e.g.,the computer system 1100 of FIG. 11 ) implementing the method 3300. Themethod 3300 may be implemented as computer instructions stored on amemory device and executable by the one or more processing devices. Incertain implementations, the method 3300 may be performed by a singleprocessing thread. Alternatively, the method 3300 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods.

In some embodiments, a system may be used to implement the method 3300.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 3300.

At block 3302, the processing device may receive one or morecharacteristics of the user. The one or more characteristics of the usermay include personal information, performance information, measurementinformation, or some combination thereof.

At block 3304, the processing device may generate, using one or moretrained machine learning models, the treatment plan for the user. Thetreatment plan may be generated based on the one or more characteristicsof the user. The treatment plan may include a dietary plan tailored forthe user to manage one or more medical conditions associated with theuser, and an exercise plan including one or more exercises associatedwith the one or more medical conditions. In some embodiments, the one ormore trained machine learning models may generate the treatment planincluding the dietary plan based on at least a comorbidity of the user,a condition of the user, a demographic of the user, a psychographic ofthe user, or some combination thereof. In some embodiments, the one ormore medical conditions pertain to cardiac health, pulmonary health,bariatric health, oncologic health, or some combination thereof.

At block 3306, the processing device may present, via the display, atleast a portion of the treatment plan including the dietary plan. Insome embodiments, the processing device modifies an operating parameterof the electromechanical machine to cause the electromechanical machineto implement the one or more exercises. In some embodiments theprocessing device may initiate, while the user performs the treatmentplan, a telemedicine session between a computing device of the user anda computing device of a healthcare professional.

In some embodiments, the processing device may receive, from one or moresensors, one or more measurements associated with the user. The one ormore measurements may be received while the user performs the treatmentplan. The processing device may determine, based on the one or moremeasurements, whether a predetermined criteria for the dietary plan issatisfied. The predetermined criteria may relate to weight, heartrate,blood pressure, blood oxygen level, body mass index, blood sugar level,enzyme level, blood count level, blood vessel data, heart rhythm data,protein data, or some combination thereof.

Responsive to determining the predetermined criteria for the dietaryplan is not satisfied, the processing device may maintain the dietaryplan and control the electromechanical device according to the exerciseplan. Responsive to determining the predetermined criteria for thedietary plan is not satisfied, the processing device may modify, usingthe one or more trained machine learning models, the treatment plan togenerate a modified treatment plan including at least a modified dietaryplan. The processing device may transmit the modified treatment plan tocause the display to present the modified dietary plan.

Clauses

Clause 1.18 A computer-implemented system, comprising:

-   -   an electromechanical machine configured to be manipulated by a        user while performing a treatment plan;    -   an interface comprising a display configured to present        information pertaining to the treatment plan; and    -   a processing device configured to:    -   receive one or more characteristics of the user, wherein the one        or more characteristics comprise personal information,        performance information, measurement information, or some        combination thereof;    -   generate, using one or more trained machine learning models, the        treatment plan for the user, wherein the treatment plan is        generated based on the one or more characteristics of the user,        and the treatment plan comprises:    -   a dietary plan tailored for the user to manage one or more        medical conditions associated with the user, and    -   an exercise plan comprises one or more exercises associated with        the one or more medical conditions; and    -   present, via the display, at least a portion of the treatment        plan comprising the dietary plan.

Clause 2.18 The computer-implemented system of any clause herein,wherein the one or more trained machine learning models generates thetreatment plan comprising the dietary plan based on at least acomorbidity of the user, a condition of the user, a demographic of theuser, a psychographic of the user, or some combination thereof.

Clause 3.18 The computer-implemented system of any clause herein,wherein the one or more conditions pertain to cardiac health, pulmonaryhealth, bariatric health, oncologic health, or some combination thereof.

Clause 4.18 The computer-implemented system of any clause herein,wherein the processing device is further to:

-   -   receive, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan; and    -   determine, based on the one or more measurements, whether a        predetermined criteria for the dietary plan is satisfied,        wherein the predetermined criteria relates to:    -   weight, heartrate, blood pressure, blood oxygen level, body mass        index, blood sugar level, enzyme level, blood count level, blood        vessel data, heart rhythm data, protein data, or some        combination thereof.

Clause 5.18 The computer-implemented system of any clause herein,wherein, responsive to determining the predetermined criteria for thedietary plan is not satisfied, the processing device is to maintain thedietary plan and control the electromechanical device according to theexercise plan.

Clause 6.18 The computer-implemented system of any clause herein,wherein, responsive to determining the predetermined criteria for thedietary plan is not satisfied, the processing device is to:

-   -   modify, using the one or more trained machine learning models,        the treatment plan to generate a modified treatment plan        comprising at least a modified dietary plan, and    -   transmit the modified treatment plan to cause the display to        present the modified dietary plan.

Clause 7.18 The computer-implemented system of any clause herein,wherein the processing device is to modify an operating parameter of theelectromechanical machine to cause the electromechanical machine toimplement the one or more exercises.

Clause 8.18 The computer-implemented system of any clause herein,wherein the processing device is to initiate, while the user performsthe treatment plan, a telemedicine session between a computing device ofthe user and a computing device of a healthcare professional.

Clause 9.18 A computer-implemented method, comprising:

-   -   receiving one or more characteristics of the user, wherein the        one or more characteristics comprise personal information,        performance information, measurement information, or some        combination thereof;    -   generating, using one or more trained machine learning models,        the treatment plan for the user, wherein the treatment plan is        generated based on the one or more characteristics of the user,        and the treatment plan comprises:    -   a dietary plan tailored for the user to manage one or more        medical conditions associated with the user, and    -   an exercise plan comprises one or more exercises associated with        the one or more medical conditions; and    -   presenting, via the display, at least a portion of the treatment        plan comprising the dietary plan.

Clause 10.18 The computer-implemented method of any clause herein,wherein the one or more trained machine learning models generates thetreatment plan comprising the dietary plan based on at least acomorbidity of the user, a condition of the user, a demographic of theuser, a psychographic of the user, or some combination thereof.

Clause 11.18 The computer-implemented method of any clause herein,wherein the one or more conditions pertain to cardiac health, pulmonaryhealth, bariatric health, oncologic health, or some combination thereof.

Clause 12.18 The computer-implemented method of any clause herein,further comprising:

-   -   receiving, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan; and    -   determining, based on the one or more measurements, whether a        predetermined criteria for the dietary plan is satisfied,        wherein the predetermined criteria relates to:    -   weight, heartrate, blood pressure, blood oxygen level, body mass        index, blood sugar level, enzyme level, blood count level, blood        vessel data, heart rhythm data, protein data, or some        combination thereof.

Clause 13.18 The computer-implemented method of any clause herein,wherein, responsive to determining the predetermined criteria for thedietary plan is not satisfied, the method further comprises maintainingthe dietary plan and control the electromechanical device according tothe exercise plan.

Clause 14.18 The computer-implemented method of any clause herein,wherein, responsive to determining the predetermined criteria for thedietary plan is not satisfied, the method further comprises:

-   -   modifying, using the one or more trained machine learning        models, the treatment plan to generate a modified treatment plan        comprising at least a modified dietary plan, and    -   transmitting the modified treatment plan to cause the display to        present the modified dietary plan.

Clause 15.18 The computer-implemented method of any clause herein,further comprising modifying an operating parameter of theelectromechanical machine to cause the electromechanical machine toimplement the one or more exercises.

Clause 16.18 The computer-implemented method of any clause herein,further comprising initiating, while the user performs the treatmentplan, a telemedicine session between a computing device of the user anda computing device of a healthcare professional.

Clause 17.18 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive one or more characteristics of the user, wherein the one        or more characteristics comprise personal information,        performance information, measurement information, or some        combination thereof;    -   generate, using one or more trained machine learning models, the        treatment plan for the user, wherein the treatment plan is        generated based on the one or more characteristics of the user,        and the treatment plan comprises:    -   a dietary plan tailored for the user to manage one or more        medical conditions associated with the user, and    -   an exercise plan comprises one or more exercises associated with        the one or more medical conditions; and    -   present, via the display, at least a portion of the treatment        plan comprising the dietary plan.

Clause 18.18 The computer-readable medium of any clause herein, whereinthe one or more trained machine learning models generates the treatmentplan comprising the dietary plan based on at least a comorbidity of theuser, a condition of the user, a demographic of the user, apsychographic of the user, or some combination thereof.

Clause 19.18 The computer-readable medium of any clause herein, whereinthe one or more conditions pertain to cardiac health, pulmonary health,bariatric health, oncologic health, or some combination thereof.

Clause 20.18 The computer-readable medium of any clause herein, whereinthe processing device is further to:

-   -   receiving, from one or more sensors, one or more measurements        associated with the user, wherein the one or more measurements        are received while the user performs the treatment plan; and    -   determining, based on the one or more measurements, whether a        predetermined criteria for the dietary plan is satisfied,        wherein the predetermined criteria relates to:    -   weight, heartrate, blood pressure, blood oxygen level, body mass        index, blood sugar level, enzyme level, blood count level, blood        vessel data, heart rhythm data, protein data, or some        combination thereof.

System and Method for an Enhanced Healthcare Professional User InterfaceDisplaying Measurement Information for a Plurality of Users

FIG. 34 generally illustrates an example embodiment of a method 3400 forpresenting an enhanced healthcare professional user interface displayingmeasurement information for a plurality of users according to theprinciples of the present disclosure. The method 3400 may be performedby processing logic that may include hardware (circuitry, dedicatedlogic, etc.), software, or a combination of both. The method 3400 and/oreach of their individual functions, subroutines, or operations may beperformed by one or more processing devices of a computing device (e.g.,the computer system 1100 of FIG. 11 ) implementing the method 3400. Themethod 3400 may be implemented as computer instructions stored on amemory device and executable by the one or more processing devices. Incertain implementations, the method 3400 may be performed by a singleprocessing thread. Alternatively, the method 3400 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods.

In some embodiments, a system may be used to implement the method 3400.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to one or more users eachusing an electromechanical machine to perform a treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 3400.

At block 3402, the processing device may receive one or morecharacteristics associated with each of one or more users. The one ormore characteristics may include personal information, performanceinformation, measurement information, or some combination thereof. Insome embodiments, the measurement information and the performanceinformation may be received via one or more wireless sensors associatedwith each of the one or more users.

At block 3404, the processing device may receive one or more video feedsfrom one or more computing devices associated with the one or moreusers. In some embodiments, the one or more video feeds may includereal-time or near real-time video data of the user during a telemedicinesession. In some embodiments, at least two video feeds are presentedconcurrently with at least two characteristics associated with at leasttwo users.

At block 3406, the processing device may present, in a respectiveportion of a user interface on the display, the one or morecharacteristics for each of the one or more users and a respective videofeed associated with each of the one or more users. Each respectiveportion may include a graphical element that presents real-time or nearreal-time electrocardiogram information pertaining to each of the one ormore users.

In some embodiments, for each of the one or more users, the respectiveportion may include a set of graphical elements arranged in a row. Theset of graphical elements may be associated with a blood pressure of theuser, a blood oxygen level of the user, a heartrate of the user, therespective video feed, a means for communicating with the user, or somecombination thereof.

In some embodiments, the processing device may present, via the userinterface, a graphical element that enables initiating or terminating atelemedicine session with one or more computing devices of the one ormore users. In some embodiments, the processing device may initiate atleast two telemedicine sessions concurrently and present at least twovideo feeds of the user on the user interface at the same time.

In some embodiments, the processing device may control a refresh rate ofthe graphical element that presents real-time or near real-timeelectrocardiogram information pertaining to each of the one or moreusers, and the refresh rate may be controlled based on theelectrocardiogram information satisfying a certain criteria (e.g., aheartrate above 100 beats per minute, a heartrate below 100 beats perminute, a heartrate with a range of 60-100 beats per minute, or thelike).

Clauses

Clause 1.19 A computer-implemented system, comprising:

-   -   an interface comprising a display configured to present        information pertaining to one or more users, wherein the one or        more users are each using an electromechanical machine to        perform a treatment plan; and    -   a processing device configured to:    -   receive one or more characteristics associated with each of the        one or more users, wherein the one or more characteristics        comprise personal information, performance information,        measurement information, or some combination thereof;    -   receive one or more video feeds from one or more computing        devices associated with the one or more users; and    -   present, in a respective portion of a user interface on the        display, the one or more characteristics for each of the one or        more users and a respective video feed associated with each of        the one or more users, wherein each respective portion comprises        a graphical element that presents real-time or near real-time        electrocardiogram information pertaining to each of the one or        more users.

Clause 2.19 The computer-implemented system of any clause herein,wherein the one or more video feeds comprise real-time or near real-timevideo data of the user during a telemedicine session.

Clause 3.19 The computer-implemented system of any clause herein,wherein at least two video feeds are presented concurrently with atleast two characteristics associated with at least two users.

Clause 4.19 The computer-implemented system of any clause herein,wherein, for each of the one or more users, the respective portioncomprises a plurality of graphical elements arranged in a row, whereinthe plurality of graphical elements are associated with a blood pressureof the user, a blood oxygen level of the user, a heartrate of the user,the respective video feed, a means for communicating with the user, orsome combination thereof.

Clause 5.19 The computer-implemented system of any clause herein,wherein the processing device is to present, via the user interface, agraphical element that enables initiating or terminating a telemedicinesession with one or more computing devices of the one or more users.

Clause 6.19 The computer-implemented system of any clause herein,wherein the processing device is to initiate at least two telemedicinesessions concurrently and present at least two video feeds of the useron the user interface at the same time.

Clause 7.19 The computer-implemented system of any clause herein,wherein the processing device controls a refresh rate of the graphicalelement that presents real-time or near real-time electrocardiograminformation pertaining to each of the one or more users, and the refreshrate is controlled based on the electrocardiogram information satisfyinga certain criteria.

Clause 8.19 The computer-implemented system of any clause herein,wherein the measurement information and the performance information isreceived via one or more wireless sensors associated with each of theone or more users.

Clause 9.19 A computer-implemented method, comprising:

-   -   receiving one or more characteristics associated with each of        one or more users, wherein the one or more characteristics        comprise personal information, performance information,        measurement information, or some combination thereof, and        wherein the one or more users are each using an        electromechanical machine to perform a treatment plan;    -   receiving one or more video feeds from one or more computing        devices associated with the one or more users; and    -   presenting, in a respective portion of a user interface on a        display of an interface, the one or more characteristics for        each of the one or more users and a respective video feed        associated with each of the one or more users, wherein each        respective portion comprises a graphical element that presents        real-time or near real-time electrocardiogram information        pertaining to each of the one or more users.

Clause 10.19 The computer-implemented method of any clause herein,wherein the one or more video feeds comprise real-time or near real-timevideo data of the user during a telemedicine session.

Clause 11.19 The computer-implemented method of any clause herein,wherein at least two video feeds are presented concurrently with atleast two characteristics associated with at least two users.

Clause 12.19 The computer-implemented method of any clause herein,wherein, for each of the one or more users, the respective portioncomprises a plurality of graphical elements arranged in a row, whereinthe plurality of graphical elements are associated with a blood pressureof the user, a blood oxygen level of the user, a heartrate of the user,the respective video feed, a means for communicating with the user, orsome combination thereof.

Clause 13.19 The computer-implemented method of any clause herein,further comprising presenting, via the user interface, a graphicalelement that enables initiating or terminating a telemedicine sessionwith one or more computing devices of the one or more users.

Clause 14.19 The computer-implemented method of any clause herein,further comprising initiating at least two telemedicine sessionsconcurrently and present at least two video feeds of the user on theuser interface at the same time.

Clause 15.19 The computer-implemented method of any clause herein,further comprising controlling a refresh rate of the graphical elementthat presents real-time or near real-time electrocardiogram informationpertaining to each of the one or more users, and the refresh rate iscontrolled based on the electrocardiogram information satisfying acertain criteria.

Clause 16.19 The computer-implemented method of any clause herein,wherein the measurement information and the performance information isreceived via one or more wireless sensors associated with each of theone or more users.

Clause 17.19 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   receive one or more characteristics associated with each of one        or more users, wherein the one or more characteristics comprise        personal information, performance information, measurement        information, or some combination thereof, and wherein the one or        more users are each using an electromechanical machine to        perform a treatment plan;    -   receive one or more video feeds from one or more computing        devices associated with the one or more users; and    -   present, in a respective portion of a user interface on a        display of an interface, the one or more characteristics for        each of the one or more users and a respective video feed        associated with each of the one or more users, wherein each        respective portion comprises a graphical element that presents        real-time or near real-time electrocardiogram information        pertaining to each of the one or more users.

Clause 18.19 The computer-readable medium of any clause herein, whereinthe one or more video feeds comprise real-time or near real-time videodata of the user during a telemedicine session.

Clause 19.19 The computer-readable medium of any clause herein, whereinat least two video feeds are presented concurrently with at least twocharacteristics associated with at least two users.

Clause 20.19 The computer-readable medium of any clause herein, wherein,for each of the one or more users, the respective portion comprises aplurality of graphical elements arranged in a row, wherein the pluralityof graphical elements are associated with a blood pressure of the user,a blood oxygen level of the user, a heartrate of the user, therespective video feed, a means for communicating with the user, or somecombination thereof.

FIG. 35 generally illustrates an embodiment of an enhanced healthcareprofessional display 3500 of the assistant interface presentingmeasurement information for a plurality of patients concurrently engagedin telemedicine sessions with the healthcare professional according tothe principles of the present disclosure. As depicted, there are fivepatients that are actively engaged in a telemedicine session with ahealthcare professional using the computing device presenting thehealthcare professional display 3500. Each patient is associated withinformation represented by graphical elements arranged in a respectiverow. For example, each patient is assigned a row including graphicalelements representing data pertaining to blood pressure, blood oxygenlevel, heartrate, a video feed of the patient during the telemedicinesession, and various buttons to enable messaging, displaying informationpertaining to the patient, scheduling appointment with the patient, etc.The enhanced graphical user interface displays the data related to thepatients in a manner that may enhance the healthcare professional'sexperience using the computing device, thereby providing an improvementto technology. For example, the enhanced healthcare professional display3500 arranges real-time or near real-time measurement data pertaining toeach patient, as well as a video feed of the patient, that may bebeneficial, especially on computing devices with a reduced screen size,such as a tablet. The number of patients that are allowed to initiatemonitored telemedicine sessions concurrently may be controlled by afederal regulation, such as promulgated by the FDA. In some embodiments,the data received and displayed for each patient may be received fromone or more wireless sensors, such as a wireless electrocardiogramsensor attached to a user's body.

System and Method for an Enhanced Patient User Interface DisplayingReal-Time Measurement Information During a Telemedicine Session

FIG. 36 generally illustrates an example embodiment of a method 3600 forpresenting an enhanced patient user interface displaying real-timemeasurement information during a telemedicine session according to theprinciples of the present disclosure. The method 3600 may be performedby processing logic that may include hardware (circuitry, dedicatedlogic, etc.), software, or a combination of both. The method 3600 and/oreach of their individual functions, subroutines, or operations may beperformed by one or more processing devices of a computing device (e.g.,the computer system 1100 of FIG. 11 ) implementing the method 3600. Themethod 3600 may be implemented as computer instructions stored on amemory device and executable by the one or more processing devices. Incertain implementations, the method 3600 may be performed by a singleprocessing thread. Alternatively, the method 3600 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods.

In some embodiments, a system may be used to implement the method 3600.The system may include the treatment apparatus 70 (electromechanicalmachine) configured to be manipulated by a user while the user isperforming a treatment plan, and an interface including a displayconfigured to present information pertaining to the treatment plan. Thesystem may include a processing device configured to executeinstructions implemented the method 3600.

At block 3602, the processing device may present, in a first portion ofa user interface on the display, a video feed from a computing deviceassociated with a healthcare professional.

At block 3604, the processing device may present, in a second portion ofthe user interface, a video feed from a computing device associated withthe user.

At block 3606, the processing device may receive, from one or morewireless sensors associated with the user, measurement informationpertaining to the user while the user uses the electromechanical machineto perform the treatment plan. The measurement information may include aheartrate, a blood pressure, a blood oxygen level, or some combinationthereof.

At block 3608, the processing device may present, in a third portion ofthe user interface, one or more graphical elements representing themeasurement information. In some embodiments, the one or more graphicalelements may be updated in real-time time or near real-time to reflectupdated measurement information received from the one or more wirelesssensors. In some embodiments, the one or more graphical elements mayinclude heartrate information that is updated in real-time or nearreal-time and the heartrate information may be received from a wirelesselectrocardiogram sensor attached to the user's body.

In some embodiments, the processing device may present, in a furtherportion of the user interface, information pertaining to the treatmentplan. The treatment plan may be generated by one or more machinelearning models based on or more characteristics of the user. The one ormore characteristics may pertain to the condition of the user. Thecondition may include cardiac health, pulmonary health, bariatrichealth, oncologic health, or some combination thereof. In someembodiments, the information may include at least an operating mode ofthe electromechanical machine. The operating mode may include an activemode, a passive mode, a resistive mode, an active-assistive mode, orsome combination thereof.

In some embodiments, the processing device may control, based on thetreatment plan, operation of the electromechanical machine.

Clauses

Clause 1.20 A computer-implemented system, comprising:

-   -   an electromechanical machine;    -   an interface comprising a display configured to present        information pertaining to a user using the electromechanical        machine to perform a treatment plan; and    -   a processing device configured to:    -   present, in a first portion of a user interface on the display,        a video feed from a computing device associated with a        healthcare professional;    -   present, in a second portion of the user interface, a video feed        from a computing device associated with the user;    -   receiving, from one or more wireless sensors associated with the        user, measurement information pertaining to the user while the        user uses the electromechanical machine to perform the treatment        plan, wherein the measurement information comprises a heartrate,        a blood pressure, a blood oxygen level, or some combination        thereof; and    -   present, in a third portion of the user interface, one or more        graphical elements representing the measurement information.

Clause 2.20 The computer-implemented system of any clause herein,wherein the one or more graphical elements are updated in real-time ornear real-time to reflect updated measurement information received fromthe one or more wireless sensors.

Clause 3.20 The computer-implemented system of any clause herein,wherein the processing device is to present, in a further portion of theuser interface, information pertaining to the treatment plan, whereinthe treatment plan is generated by one or more machine learning modelsbased on one or more characteristics of the user.

Clause 4.20 The computer-implemented system of any clause herein,wherein the one or more characteristics pertain to condition of theuser, wherein the condition comprises cardiac health, pulmonary health,bariatric health, oncologic health, or some combination thereof.

Clause 5.20 The computer-implemented system of any clause herein,wherein the information comprises at least an operating mode of theelectromechanical machine, wherein the operating mode comprises anactive mode, a passive mode, a resistive mode, an active-assistive mode,or some combination thereof.

Clause 6.20 The computer-implemented system of any clause herein,wherein the processing device is to control, based on the treatmentplan, operation of the electromechanical machine.

Clause 7.20 The computer-implemented system of any clause herein,wherein at least one of the one or more graphical elements comprisesheartrate information that is updated in real-time or near real-time,and the heartrate information is received from a wirelesselectrocardiogram sensor attached to the user's body.

Clause 8.20 A computer-implemented method, comprising:

-   -   presenting, in a first portion of a user interface on a display,        a video feed from a computing device associated with a        healthcare professional;    -   presenting, in a second portion of the user interface, a video        feed from a computing device associated with the user;    -   receiving, from one or more wireless sensors associated with the        user, measurement information pertaining to the user while the        user uses an electromechanical machine to perform a treatment        plan, wherein the measurement information comprises a heartrate,        a blood pressure, a blood oxygen level, or some combination        thereof; and    -   presenting, in a third portion of the user interface, one or        more graphical elements representing the measurement        information.

Clause 9.20 The computer-implemented method of any clause herein,wherein the one or more graphical elements are updated in real-time ornear real-time to reflect updated measurement information received fromthe one or more wireless sensors.

Clause 10.20 The computer-implemented method of any clause herein,further comprising presenting, in a further portion of the userinterface, information pertaining to the treatment plan, wherein thetreatment plan is generated by one or more machine learning models basedon one or more characteristics of the user.

Clause 11.20 The computer-implemented method of any clause herein,wherein the one or more characteristics pertain to condition of theuser, wherein the condition comprises cardiac health, pulmonary health,bariatric health, oncologic health, or some combination thereof.

Clause 12.20 The computer-implemented method of any clause herein,wherein the information comprises at least an operating mode of theelectromechanical machine, wherein the operating mode comprises anactive mode, a passive mode, a resistive mode, an active-assistive mode,or some combination thereof.

Clause 13.20 The computer-implemented method of any clause herein,further comprising controlling, based on the treatment plan, operationof the electromechanical machine.

Clause 14.20 The computer-implemented method of any clause herein,wherein at least one of the one or more graphical elements comprisesheartrate information that is updated in real-time or near real-time,and the heartrate information is received from a wirelesselectrocardiogram sensor attached to the user's body.

Clause 15.20 A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

-   -   present, in a first portion of a user interface on a display, a        video feed from a computing device associated with a healthcare        professional;    -   present, in a second portion of the user interface, a video feed        from a computing device associated with the user;    -   receive, from one or more wireless sensors associated with the        user, measurement information pertaining to the user while the        user uses an electromechanical machine to perform a treatment        plan, wherein the measurement information comprises a heartrate,        a blood pressure, a blood oxygen level, or some combination        thereof; and    -   present, in a third portion of the user interface, one or more        graphical elements representing the measurement information.

Clause 16.20 The computer-readable medium of any clause herein, whereinthe one or more graphical elements are updated in real-time or nearreal-time to reflect updated measurement information received from theone or more wireless sensors.

Clause 17.20 The computer-readable medium of any clause herein, whereinthe processing device is further to, in a further portion of the userinterface, information pertaining to the treatment plan, wherein thetreatment plan is generated by one or more machine learning models basedon one or more characteristics of the user.

Clause 18.20 The computer-readable medium of any clause herein, whereinthe one or more characteristics pertain to condition of the user,wherein the condition comprises cardiac health, pulmonary health,bariatric health, oncologic health, or some combination thereof.

Clause 19.20 The computer-readable medium of any clause herein, whereinthe information comprises at least an operating mode of theelectromechanical machine, wherein the operating mode comprises anactive mode, a passive mode, a resistive mode, an active-assistive mode,or some combination thereof.

Clause 20.20 The computer-readable medium of any clause herein, whereinthe processing device is further to, based on the treatment plan,operation of the electromechanical machine.

FIG. 37 generally illustrates an embodiment of an enhanced patientdisplay 3700 of the patient interface presenting real-time measurementinformation during a telemedicine session according to the principles ofthe present disclosure. As depicted, the enhanced patient display 3700includes two graphical elements that represent two real-time or nearreal-time video feeds associated with the user and the healthcareprofessional (e.g., observer). Further, the enhanced patient display3700 presents information pertaining to a treatment plan, such as a mode(Active Mode), a session number (Session 1), an amount of time remainingin the session (e.g., 00:28:20), and a graphical element speedometerthat represents the speed at which the user is pedaling and providesinstructions to the user.

Further, the enhanced patient display 3700 may include one or moregraphical elements that present real-time or near real-time measurementdata to the user. For example, as depicted, the graphical elementspresent blood pressure data, blood oxygen data, and heartrate data tothe user and the data may be streaming live as the user is performingthe treatment plan using the electromechanical machine. The enhancedpatient display 3700 may arrange the video feeds, the treatment planinformation, and the measurement information in such a manner thatimproves the user's experience using the computing device, therebyproviding a technical improvement. For example, the layout of thedisplay 3700 may be superior to other layouts, especially on a computingdevice with a reduced screen size, such as a tablet or smartphone.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present disclosure. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

The various aspects, embodiments, implementations, or features of thedescribed embodiments can be used separately or in any combination. Theembodiments disclosed herein are modular in nature and can be used inconjunction with or coupled to other embodiments.

Consistent with the above disclosure, the examples of assembliesenumerated in the following clauses are specifically contemplated andare intended as a non-limiting set of examples.

1. A computer-implemented system, comprising: one or more processingdevices configured to receive attribute data associated with a user,generate, based on a pulmonary condition of the user, a selected set ofthe attribute data, determine, based on the selected set of theattribute data, a first probability of improving a pulmonary conditionof the user subsequent to at least one of a pulmonary procedure beingperformed on the user, a pulmonary treatment being performed on theuser, and a pulmonary diagnosis, and generate, based on the firstprobability, a treatment plan that includes one or more exercisesdirected to modifying the first probability; and a treatment apparatusconfigured to enable implementation of the treatment plan.
 2. Thecomputer-implemented system of claim 1, wherein the attribute dataincludes data associated with a pulmonary health of the user.
 3. Thecomputer-implemented system of claim 1, wherein the one or moreprocessing devices are configured to execute an attribute data model,and wherein, to generate the selected set of the attribute data, theattribute model is configured to at least one of assign weights to theattribute data, rank the attribute data, and filter the attribute data.4. The computer-implemented system of claim 3, wherein the one or moreprocessing devices are configured to execute a probability model,wherein the probability model is configured to determine the firstprobability.
 5. The computer-implemented system of claim 4, wherein theone or more processing devices are configured to execute a treatmentplan model, wherein the treatment plan model is configured to generatethe treatment plan to modify the first probability.
 6. Thecomputer-implemented system of claim 1, wherein the one or moreprocessing devices are further configured, based on the selected set ofthe attribute data, to generate a second probability that the user willbe eligible for the at least one of the pulmonary procedure and thepulmonary treatment.
 7. The computer-implemented system of claim 6,wherein the one or more processing devices are configured to at leastone of (i) generate the treatment plan further to modify the secondprobability and (ii) generate a recommendation of whether the usershould undergo the at least one of the pulmonary procedure and thepulmonary treatment.
 8. The computer-implemented system of claim 7,wherein, subsequent to implementing the treatment plan using thetreatment apparatus, the one or more processing devices are configured,based on the recommendation, to modify the treatment plan.
 9. Thecomputer-implemented system of claim 8, wherein the one or moreprocessing devices are configured to transmit the modified treatmentplan to cause the treatment apparatus to implement at least one modifiedexercise of the modified treatment plan.
 10. The computer-implementedsystem of claim 1, wherein, while the user performs the treatment plan,the one or more processing devices are configured to initiate atelemedicine session between a computing device of the user and acomputing device of a healthcare professional.
 11. Thecomputer-implemented system of claim 1, wherein the one or moreprocessing devices are configured, based on a respiratory exertionthreshold associated with dyspnoea experienced by the user, to modifythe treatment plan.
 12. The computer-implemented system of claim 11,wherein the one or more processing devices are configured, based onperformance information indicative of characteristics of the usermeasured while the user performs the treatment plan, to modify therespiratory exertion threshold.
 13. A method, comprising: using one ormore processing devices, receiving attribute data associated with auser, generating, based on a pulmonary condition of the user, a selectedset of the attribute data, determining, based on the selected set of theattribute data, a first probability of improving a pulmonary conditionof the user subsequent to at least one of a pulmonary procedure beingperformed on the user, a pulmonary treatment being performed on theuser, and a pulmonary diagnosis, and generating, based on the firstprobability, a treatment plan that includes one or more exercisesdirected to modifying the first probability; and using a treatmentapparatus to implement the treatment plan.
 14. The method of claim 13,wherein the attribute data includes data associated with a pulmonaryhealth of the user.
 15. The method of claim 13, further comprising,using the one or more processing devices, executing an attribute datamodel, and wherein, to generate the selected set of the attribute data,the attribute model at least one of assigns weights to the attributedata, ranks the attribute data, and filters the attribute data.
 16. Themethod of claim 15, further comprising, using the one or more processingdevices, executing a probability model, wherein the probability model isconfigured to determine the first probability.
 17. The method of claim16, further comprising, using the one or more processing devices,executing a treatment plan model, wherein the treatment plan model isconfigured to generate the treatment plan to modify the firstprobability.
 18. The method of claim 13, further comprising, using theone or more processing devices, generating, based on the selected set ofthe attribute data, a second probability that the user will be eligiblefor the at least one of the pulmonary procedure and the pulmonarytreatment.
 19. The method of claim 18, further comprising, using the oneor more processing devices, at least one of (i) generating the treatmentplan further to modify the second probability and (ii) generating arecommendation of whether the user should undergo the at least one ofthe pulmonary procedure and the pulmonary treatment.
 20. The method ofclaim 19, further comprising, using the one or more processing devicessubsequent to implementing the treatment plan using the treatmentapparatus, modifying, based on the recommendation, the treatment plan.21. The method of claim 20, further comprising, using the one or moreprocessing devices, transmitting the modified treatment plan to causethe treatment apparatus to implement at least one modified exercise ofthe modified treatment plan.
 22. The method of claim 13, furthercomprising, while the user performs the treatment plan, using the one ormore processing devices to initiate a telemedicine session between acomputing device of the user and a computing device of a healthcareprofessional.
 23. The method of claim 13, further comprising using theone or more processing devices to, based on a respiratory exertionthreshold associated with dyspnoea experienced by the user, modify thetreatment plan.
 24. The method of claim 23, further comprising using theone or more processing devices to, based on performance informationindicative of characteristics of the user measured while the userperforms the treatment plan, modify the respiratory exertion threshold.